# Re-architecting PeerChat for on-demand mental health support โ€” Nadine Raydan, Emily Smith โ€” session 2026-08-27T00:30:00.000Z โ†’ 2026-08-27T01:00:00.000Z _446 transcript lines ยท 98 slides_ ## Transcript some absolutely fantastic work in the youth mental health space. They've been doing it for a long, long time. Big fans of the team and their work and really happy that we get to hear to hear from the people. From two of their finest this morning. No. That's okay. Are we ready? Thank you so much. Almost. Almost. Last little details. So those two thumbs up were premature, John. There you go. Are you comfortable? Yep. That's good. There you go. I just have to It's all very good. Alright. Cool. I Here we go. Mhmm. Alright. Please join me in welcoming Nadine and Emily to the stage. Thanks for your patience, everyone. We got set up. Yeah. It's interesting because, context and custodians of context is exactly what we're gonna talk about with m being AP Work team lead. Yes. The ultimate custodian of context. Yeah. Hello, everyone. So to start us off, are we open to that? I do have a stutter, how to hold life, I'm just open about it. So I'm less, like, nervous. So if you hear me pause or hear my words break out, hence why. Also, heads up, like we will be talking about like young people and the mental health. So heavier topics that may come up. If, you like you need a moment, like, it's okay to step away and we will be here afterwards. Like if you wanna have a chat. Awesome. Thanks, Sam. We'll try and do it this way. I know an acknowledgment of country was already given this morning, but, thanks for having us. We're happy to be here with you on Gadigal Land. This land was never ceded, always was, always will be aboriginal land. We also recognize our connection to country as integral to health and well-being. Yeah. I guess you already heard like a little bit about us, but, context over the last year we supported over 2.2 and a, like, million like young people, who like needed that like, anonymous and confidential support. So the model works from broad reach to scale depth. Over on the left pillar, you see the expert mental health content. We really, are positioned as a digital first step model within the mental youth, health landscape. The service supports young people to take small steps to feel better, by using human connection and technology that co designed with young people and grounded in evidence. Within that broader landscape, this really looks like earlier interventions, and non clinical support. That broad reach self guided content then moves into the middle pillar which is AI. It's a it's a chatbot called us reach out, and it allows young people to have short focused conversations in their own words. It draws from our extensive library of evidence informed content and provides service recommendations where speaking to a human is the next best step. And that's where we really move into that third pillar, which is the deepest level of impact and relational care, and that's peer chat. While fewer young people engage with peer chat, those are do are seeking human connection to navigate everyday issues with varying degrees of complexity. For some young people, the very first need that they have when starting a conversation is to verify that they're actually speaking to a human and not AI. And it demonstrates that that kind of authentic human connection is the core user need that sits above all others. So, yeah. What is like a picture? This is where I live and breathe, I guess. We are in, like, one to one text, like, base. On like chat, like over web chat when, like, young people are able to just hop on. Like then and there and just have a chat and like in the moment, like around like whatever's like on their mind. And then on like, the other end of the chat is one, like, of our peer workers who have their own experience in that journey of, I guess, life and mental health, and like they really, like, use that in a way that helps the young person, like, feel heard, under and left alone. We are, like, one of like, the only, like, on demand like, chats, operating under, like, a p work like model in the country. And we are co designed like, by young people like for young people. And I was actually like, one of those young people. I started helping on the project when I was 20 years old, like, when it was, like, a tiny idea. So, been really cool to watch it grow and, I, like, I guess and and, like, be all around, like, young people. I've talked about, like, we operate under like, p work model, So what that means is like we're really using our lived and living experience to use this in a way where it has purpose, we're able to talk about it openly, like with a young person, where like, recovery orientated, like, young person led. Like, we aren't here as, like, a friend or like a counselor, aren't here to give him like advice or to diagnose however we are here to use those like, active listening and, like, validation skills. To really help, like empower young people in their journey. So, it's really important that we continue to evolve the service in a way that remains, person centered and focused on that centrality of the one one human connection. So a young person can be, for example, smoothly handed over from, our screech out, the increasingly conversational AI chatbot. The core need is design session model, so it's up to forty five minutes. Young people it's very low friction and simple and safe, the onboarding. Yes. But they do have to verify phone number. That's a more recent introduction of friction. Young people then, if they do return can opt in to have an AI chat summary safe that's only visible to a peer worker but again, it's their choice. Then they go through a single question deterministic flow, and then the core experience is very human. It's all contained in the messenger. And if this plays, you might get to see that in action. So this is really how a young person experiences the service. It's a simulated conversation. So, not real. You'll see the men's to the flow. The peer worker manages the entire conversation in the messenger. Just forward a bit so you get a bit of a sense of how the conversation happens. Buku Romy, he's one of our team leads. Cool. So, yeah, we've talked about that we're operating on an on demand model. However, we weren't always on an on demand model. We started as, like, a booked model. But, like you can imagine, like, young people and online, like free service, that didn't work as well because, like, not as many young people would actually turn up to their booking. We were having like, young people, like SMSL and like booking, like a number of all hours of the night, like they would, like try and hop on right away. So, and with AI, we can of also, like, noticed that it was having an impact on how, like, young people wanted to, like, engage and how they really like, wanted it, like then and there. So, to meet, like, the needs of our young people, and also like, utilize our team. We then were, like, able to pivot to the on demand model. Like, we were talk about, like, the changes in more like detail but, you know, it did like, mean, like, we had to increase like the sizes of our team. We had to add layers of leadership roles. We had to then, like, also change our like, digital and data, in like for structure as well. And then we as, like, a result of all that, we had more like, young people hop hopping on in, like, active high like just stress, and it led to having more external s escalations like to ambulance services. And, like, police. And then we also didn't, like, notice that what like, young people were hoping on to talk about was also changing as well. But it's really young people's experience that has experience gone through the, undergone the most radical change really. We now know that young people engage with the on demand modality at twice the rate that they were with the previous booked, model. So that means we're supporting more young people than ever before. In some ways that kind of simplicity of the entire proposition and its experience really portrays the complexity of embedding a lived experience workforce, in a product largely product led organization within a mental health sector. So how do we co create with our lived experience workforce? Yes. So I guess, like, a core like part of how we work is really working together, having all like, voices heard and, like, taking into consideration consideration. So what this meant is we had our like peer workers who are actually like on the ground running chats, actually involved in every stage, you know, like in discovery, exploring tech options, testing an implementation, around like recruitment, like onboarding, like training, all of that. Known like decisions and like changes. And like frameworks happen like without us like involving like all of the teams listed above. We're all I guess like mutually like, informing each other. I guess, like, as an example Like, we worked with the social impact team, to change what impact looks like on demand and how we aim to better measure that, like with the ink crease of the x suspected active high like just stress, we worked with the clinical governance team around how are we able to really like implement our like, duty and lack of care like frameworks and escalation. Like, paraphrase, and then we like, of course, worked really closely in, like, with the product team around, I guess, psych almost everything, you know, our new, like, tech creating, like workshops, opportunities, like where the peer workers are able to learn in a way where it's quite hands on and like they feel confident and equipped. So switching modalities, was initially piloted. We were able to prove the model actually quite quickly within three months during that pilot, we ran the book sessions alongside the on demand modality, but the 40% utilization of the booked model really didn't shift, while the on demand rate quickly surpassed it. So we sunset the booked model and focused entirely on delivering instant care. The service rapidly matured. It was through a series of really rapid iterations. And in parallel, which we've noted, in the background, the organize was also developing an AI chatbot for young people to use. So So co creation acting is the bridge between Em's team and the technical kind of strategic design from product We applied all the same codesign principles that we'd use working with young people to develop the service as a whole, but this time to our internal peer, lived experience workforce. It's where it became very clear that it wasn't just a pivot The new service had its own distinct platform, logics, rhythms. And a scale that presented more challenges, but also a lot more opportunity for structured, automated, and assistive, tools for the team. But they really needed to come alive as a representation of what I discovered was actually a lot of internalized thinking in the team. Yeah. It's where two really different disciplines that they call can complement each other and work incredibly well if care is taken to understand why, but also how some processes came to be, even if that means wrangling multiple off platform, logics with a lot of embedded thinking in them. So three problems to kind of solve quickly emerged in this new modell and we wanted to solve them with the lived experience team. Embedded. One of the first problems was that we had these data models that, weren't adaptive from the booked model to the on demand model. So the way we classified conversations, was operational and business logic. It was very partial or completed. It really told us nothing about the way young people are engaging with the service, or their underlying needs, and it wasn't an effective translation of happy workers conceptualize the conversations or their attributes. Similarly with, like, the topic taxonomy and service team reporting taking hours, and we wanted to center that diversity of the peer workers' professional and personal judge judgments and map these very human concepts onto how the service and young people's engagement with it were evolving and to co create what ended up looking like a kind of harmonious classifier, but also one that guided peer workers to make decisions at the end of the chat that then flow through all the data model logics. We started in a pretty familiar place for a lot of people, with analyzing a lot of data, including a thousand de identified randomized transcripts, and one of our first findings was that time wasn't really a determinative factor. What that means is that a young person can experience real impact and engagement at a fifteen minute mark or a forty five minute mark. So that operational metric flattened or meaning, and if session duration isn't the major factor, then what really defines impact? To find out, we co created with our, lived experience peer workforce and really tried to ground that in daily peer practice not abstract categories. It allowed us to look at where team judgments are aligned and where they diverged We overlaid time onto the transcripts and then we spotted these really large zones of inactivity. In the data, it actually just looks like disengagement from a young person. Without peer worker annotation, we didn't really understand what was happening. But what can be happening for a young person when they pause during a conversation with a peer worker, is that they can be building courage to articulate something really difficult They can be re responding to prompt prompts from a peer worker. They can be reflecting on underlying issues or they can be switching physical settings like leaving school, stepping off a bus, going to work. Even sudden drop offs in a conversation became a lot clearer. So often it was simply things like, dinner's ready, I've gotta go, or life getting in the way. We also embedded strength space language into our data models. Moving away from like a tendency to use very online vernacular around certain behaviors, especially when young people engage in unhealthy ways, So things like trolling really hides underlying needs and it's not inclusive, it's not strength based. So we moved away from that altogether. All of this led us to really flip our classifier logics and anchor it in peer worker principles of mutuality, and reciprocity. An impactful session, as we've established, isn't about time. But did the young person gain perspective from speaking to a peer worker? And was the peer worker able to walk alongside them? The in app classifiers now use this progressive logic to evaluate really meaningful attributes like the disclosure of core concerns from a young person. With the effectiveness of peer support where any duty of care actions or interventions raised during that conversation? And how did the conversation end? So implementing the classifiers allowed us to, also for reduce data retention. So by design, we try and men maintain a really minimal dataset. We now routinely redact and delete conversations, We only leave metadata traces and it supports the privacy of the young person. So the classifiers are also really dynamic. The feedback loop is directly informed by peer practice every day, and a recent analysis shows us that pregnancy fears and porn or porn addiction will form part of the next iteration because they're coming up in our This also helps the team strengthen, so support for peer workers to deal with these conversations. But also to design trigger pathways and escalations to other services that are more appropriate for a lot of those things. We also better understand where there are queue drop offs, so that we can design better waiting experiences and offer alternate types of support for this otherwise hidden demand. It's also sorry, well, I flip. Also helped us understand a lot about what do young people wanna talk about and when. So taking a broader view from national data, we know that young people are facing the highest rates of mental ill health of any group and the least access to help. There's so many compounding factors that face young people including high psychological distress, cost of living, study and or work pressures, and cost of living can actually be a barrier to both study and work. But also to seeking professional support. So very compounded we also see increasing diagnosis and shamefully intimate violence is a top risk factor for young women. I say this because you might be surprised to see abuse, trauma and harassment rates so high in our daily demand. Yeah, what you see there is really all the macro pressures translate directly into everyday human issues for which young people want to speak to another human. You'll see that relationships, and in particular, we find romantic relationships and break ups, present at the highest volume every single day. It's completely unsurprising that young people wanna speak to a human to understand interpersonal relationships and how to navigate relational conflict. It's also completely unsurprising that we see that young people who were handed over from our AI chatbot over represent on gender and sexuality because these are also really complex things that they wanna talk to a human about. Yeah. So I guess one of our, like, focus areas was in ensuring that even, like, when we scale, like we were able to uphold the safety. Like, for both our young people, like but also our staff. So I guess, like, one of the ways that we do this is around, like, the language, like, we're using. Is it like, trauma informed, like young person led, like, we're currier? Like orientated and very much, strengths based. We also, I talked about earlier we noticed we were having more, like, young people hop on and active high like distress, and that often meant, like, we were calling other external agent to help us out and having, I guess, limited, like identifying, like information, often like made it hard to get them that imminent help. That they needed. So what we did is we, have, like, recently added out one time like, password. Like, which helps us, like, verify the numbers and I guess, like, know that they're real. So that we can get them that help, as urgently. As possible. Young people can also opt in to have an AI summary saved, as I mentioned. A bit earlier, but we only released that kind of return service user feature around a month ago, and we already know about 18% of our young people are opting in to have that summary saved even though it's only visible to a peer worker. They're automated, they're AI generated, but really importantly for this piece of work, we worked with a young developer in our team. Who's also a peer worker team lead in EN's team. So we really had those safeguards embedded from the ground up. We chose to architect a very lightweight solution here, but the prompt adherence to the peer worker framework and principles is bound by strict constraints so that we can do our best to contain what's returned so that it doesn't infer things like motivation and emotion. It doesn't speculate on the contents of the conversation. It de identifies the peer worker and the young person. And it's free of judgment. So it's designed to emulate the most ideal conversation with a peer worker really. So, yeah, we end this stand that with everything happening in their lives, like, we're not gonna be able to meet all of a young person's needs. And, you know, I guess the scope and boundaries of our role So we also acknowledge that it can be really like tricky and overwhelming on a young person to know, okay, like where do I go, when and, you know, like how how do I get that other help? So, we are able to like, recommend other options in a really, warm like gentle and like informative way, which allows like the young person to have the opportunity to ask more questions around like how does that service work or kind of, like, what is it? Look like? So we're able to help them I guess, like feel more confident. And, like, empowered to take that like, next step. So, yeah, designing an offboarding experience as a connection to another service is a bit counter to a lot of the product thinking that we're taught. Definitely counter to commercial product thinking, but two things had to happen here. A framework for human assessment and in platform affordances to seamlessly recommend the young person to a formal partners. These partners have different types of care, different stages along the spectrum of need. Peer workers identify the young level of readiness. Would will look like, and that's really important. We know that from co designing with young people that when we meet all these cool needs that they have, they can and will fluidly move between services. And that's a really great thing. Our proof of concept shows that it was about 8% of conversations that resulted in a pathway to external care. And that care provider was specifically more ongoing complex clinical needs. Really importantly, once we look at the data from that partner, we understand that young people who were referred from peer chat present with actual moderate high or acute needs already in their service. So that means we're referring a young person to the right service at exactly the right time, and that's that's a really positive move to reduce the care gap. And, yeah, unlike a lot of user experiences that are designed to kind of reduce exit, boost retention, We prioritize those cross sector partnerships to reduce the care gap and increase what many in the sector call connected care. Gonna hand over to Em just to finish our files, finish us off on impact, which actually looks kind of made up. It's so good. Yeah. It it is real. I've heard you say, at at the end of each of the chats, and, like, young people are able to answer some off like boarding questions. And you can see here it's really evident like young people are like feeling heard, understood, like better, like in that, like, moment after the chat. And like writing us quite highly We also have a box, when you can, like, type in if you have any extra like feedback. And it's really lovely actually watching them write about like the peer worker or writing a really nice notes about how it's helped them. And I guess overall, we hear like, young people, I really like how it's available. ## Slides ### 00:00:10 # support for young people REACHOUT [Abstract rounded shapes in various colors on a purple background] [REACHOUT logo with a speech bubble icon] ### 00:00:31 # support for young people REACHOUT [Illustration of several large, rounded, pastel-colored shapes on a purple background] ### 00:00:47 # Support for young people REACHOUT [Abstract design with large, rounded, colorful shapes on a purple background] ### 00:00:51 # Support for young people REACHOUT [Abstract illustration of overlapping rounded shapes in various pastel colors on a purple background] ### 00:01:14 # Support for young people [Logo for REACHOUT, featuring a speech bubble icon within the text. Abstract rounded shapes in various pastel colors (white, light green, light pink, light blue, light yellow) on a purple background.] ### 00:01:37 # Scaling safely Re-architecting **PeerChat** for on-demand mental health support for young people REACHOUT ReachOut.com [Abstract illustration with various overlapping rounded rectangles and circles in green, purple, yellow, red, pink, and blue] ### 00:02:03 ### Screenshot of the ReachOut website homepage [Screenshot of the ReachOut website homepage, featuring a prominent search bar with the text "WHATEVER'S ON YOUR MIND, ASK REACHOUT", various images of young people, and sections for peer support and First Nations wellbeing.] ### 00:02:26 # WHATEVER'S ON YOUR MIND, ASK REACHOUT [Screenshot of the ReachOut website, an online mental health support platform for young people, featuring a prominent search bar and sections for peer support, First Nations wellbeing, and articles.] ### 00:02:47 # Broad reach to scaled depth - **Expert MH content** **Most** young people will start here - Help me gain understanding & confidence - **Ask ReachOut** **Some** young people will go further - Give me small steps & things to try - **Human support** **Fewer** will need deeper support - Connect me with someone who understands Reach/Scale 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### 00:02:48 # Broad reach to scaled depth - **Expert MH content** **Most** young people will start here - Help me gain understanding & confidence - **Ask ReachOut** Some young people will go further - Give me small steps & things to try - **Human support** **Fewer** will need deeper support - Connect me with someone who understands Reach/Scale ---------------------------------------------------------------- Depth/Impact Note: Not to scale [Diagram showing three connected, rounded rectangular boxes, decreasing in size from left to right, representing a progression from "Broad reach" to "scaled depth". Each box has an icon above it: a yellow face with a monocle, a question mark, and a heart in hands.] ### 00:03:12 # Broad reach to scaled depth - **Expert MH content** - **Most** young people will start here - Help me gain understanding & confidence - **Ask ReachOut** - **Some** young people will go further - Give me small steps & things to try - **Human support** - **Fewer** will need deeper support - Connect me with someone who understands Note: Not to scale [Diagram showing three connected, decreasing-sized boxes from left to right, representing a progression from "Reach/Scale" to "Depth/Impact". The first box has an emoji with a monocle, the second a question mark, and the third a heart inside a person icon.] ### 00:03:33 # Broad reach to scaled depth - **Expert MH content** **Most** young people will start here - Help me gain understanding & confidence - **Ask ReachOut** Some young people will go further - Give me small steps & things to try - **Human support** **Fewer** will need deeper support - Connect me with someone who understands Note: Not to scale [Diagram showing three connected boxes, decreasing in size from left to right, representing a progression from "Reach/Scale" to "Depth/Impact". The boxes are labeled "Expert MH content", "Ask ReachOut", and "Human support", each with an icon and a call to action.] ### 00:03:47 # Broad reach to scaled depth - **Expert MH content** - **Most** young people will start here - Help me gain understanding & confidence - **Ask ReachOut** - Some young people will go further - Give me small steps & things to try - **Human support** - **Fewer** will need deeper support - Connect me with someone who understands Note: Not to scale [Diagram showing three stages of support: Expert MH content, Ask ReachOut, and Human support, arranged along an axis from "Reach/Scale" to "Depth/Impact". Each stage has an icon: a face with a monocle, a question mark, and a heart with hands.] ### 00:04:09 ## PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up of hands holding and typing on a smartphone] [Illustration of two speech bubbles] ### 00:04:33 ## PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up image of hands holding a glowing smartphone, illustrating a text-based chat service] [Speech bubble icon] ### 00:04:48 # PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up of hands holding a glowing smartphone] [Icon of two speech bubbles] ### 00:05:09 ## PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up of hands holding and typing on a glowing smartphone] [Speech bubble icon with three dots] ### 00:05:35 ## PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up of hands holding a glowing smartphone, typing on the screen] [Icon of two speech bubbles] ### 00:05:46 ## PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up of hands holding a glowing smartphone, illustrating a text-based chat] [Speech bubble icon] ### 00:06:10 # PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up of hands holding a glowing smartphone, with a chat bubble icon] ### 00:06:34 # PeerChat is... ...a free 1:1 text-based chat service for 16-25 year olds to discuss what's on their mind, with a trained peer worker who uses their own lived experience to **help young people feel heard, less alone and understood.** > Peer Work is a recognised profession in the mental health sector that acknowledges lived/living experience as a qualification and type of expertise. [Close-up image of hands holding a glowing smartphone] [Icon of two speech bubbles] ### 00:06:47 # Human connection ## Simple, safe onboarding - **Low-friction access** OTP increases safety for young people and peer workers, ensuring we can get young people emergency support if needed. ### 00:06:54 # Human connection ## Simple, safe onboarding - **Low-friction access** OTP increases safety for young people and peer workers, ensuring we can get young people emergency support if needed. ### 00:07:18 # Human connection ## Simple, safe onboarding - **Low-friction access** OTP increases safety for young people and peer workers, ensuring we can get young people emergency support if needed. ### 00:07:42 ### Free, anonymous, online support [Screenshot of the ReachOut website's PeerChat service] ### 00:07:44 ### Free, anonymous, online support [Screenshot of the ReachOut website's PeerChat service] ### 00:08:08 ### Enter your one time password [Screenshot of a mobile app showing a one-time password entry screen, with a hand holding a phone to the right] ### 00:08:29 # Chat with Kuromi [Screenshot of a mobile chat interface] ### 00:08:47 # From predictable booking to **real-time** scale [Abstract illustration of white rounded shapes on a purple background, with an "R" logo and speech bubble icon in the bottom right corner] ### 00:08:50 # From predictable booking to **real-time** scale [Abstract illustration of various rounded off-white shapes on a purple background] ### 00:09:10 # From predictable booking to **real-time** scale [Abstract illustration of various rounded, light-colored shapes on a purple background] ### 00:09:32 # From predictable booking to real-time scale [Abstract illustration of various white rounded shapes (circles and rounded rectangles) on a purple background] ### 00:09:48 # From predictable booking to real-time scale [Abstract design of white rounded shapes (ovals and circles) on a purple background] ### 00:09:51 # From predictable booking to real-time scale [Abstract design of white rounded shapes (ovals and circles) on a purple background] ### 00:10:12 # From predictable booking to **real-time** scale [Abstract illustration of cream-colored rounded shapes (circles and ovals) on a purple background] ### 00:10:35 ## From predictable booking to **real-time** scale [Abstract illustration of various white rounded shapes on a purple background] ### 00:10:46 # From predictable booking to **real-time** scale ### 00:11:08 ### Key result: Young people engage with **on-demand** at more than **twice the rate of the booked model**. [Illustration of a yellow star in a purple circle and a yellow waving hand in a green circle] [Close-up photo of hands holding a glowing smartphone] ### 00:11:28 # Co-creating with a lived experience workforce [Abstract illustration of several off-white rounded shapes (circles and rounded rectangles) on a pink background] ### 00:11:47 ### What does thoughtful collaboration look like? - Peer workforce - Clinical governance - Social impact - Product, design, technology & marketing [Venn diagram showing four overlapping circles labeled "Peer workforce", "Clinical governance", "Social impact", and "Product, design, technology & marketing"] ### 00:12:08 ### What does thoughtful collaboration look like? [Venn diagram with four overlapping circles labeled: Peer workforce, Clinical governance, Social impact, and Product, design, technology & marketing] ### 00:12:28 ### What does thoughtful collaboration look like? [Venn diagram with four overlapping circles labeled "Peer workforce", "Social impact", "Clinical governance", and "Product, design, technology & marketing"] ### 00:12:47 ## What does thoughtful collaboration look like? [Venn diagram with four overlapping circles: "Peer workforce", "Clinical governance", "Social impact", and "Product, design, technology & marketing"] ### 00:13:09 # What does thoughtful collaboration look like? - Peer workforce - Social impact - Clinical governance - **Product, design, technology & marketing** [Venn diagram with four overlapping circles representing "Peer workforce", "Social impact", "Clinical governance", and "Product, design, technology & marketing"] ### 00:13:31 # What does thoughtful collaboration look like? - Peer workforce - Clinical governance - Social impact - Product, design, technology & marketing [Venn diagram with four overlapping circles labeled "Peer workforce", "Clinical governance", "Social impact", and "Product, design, technology & marketing"] ### 00:13:47 ### What does thoughtful collaboration look like? [Venn diagram with four overlapping circles: Peer workforce, Clinical governance, Social impact, and Product, design, technology & marketing] ### 00:14:08 ### What does thoughtful collaboration look like? [Venn diagram with four overlapping circles: "Peer workforce", "Social impact", "Clinical governance", and "Product, design, technology & marketing"] ### 00:14:28 ## Challenges with scaling a service in a **person-centred** model of care ### 00:14:47 # Challenges with scaling a service in a *person-centred* model of care [Abstract white rounded shapes on a yellow background] ### 00:14:49 # Challenges with scaling a service in a *person-centred* model of care [Abstract white rounded shapes on a yellow background] ### 00:15:11 # Challenges with scaling a service in a *person-centred* model of care [Abstract illustration of overlapping white rounded shapes on a yellow background] ### 00:15:32 # Challenges with scaling a service in a **person-centred** model of care ### 00:15:46 # Challenges with scaling a service in a **person-centred** model of care ### 00:16:07 # Challenges with scaling a service in a **person-centred** model of care [Abstract background with large, rounded white shapes] ### 00:16:29 ### Challenge 1: HMW create data models that represent *human connection* and *lived experience* in peer work? [Abstract illustration of various purple rounded shapes (circles and rounded rectangles) on the right side of the slide] ### 00:16:48 ### Challenge 1: HMW create data models that represent **human connection** and **lived experience** in peer work? [Abstract pattern of purple rounded shapes on the right side of the slide] [Small "R๐Ÿ’ฌ" logo in the bottom right corner] ### 00:17:09 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected thought bubbles with questions/statements, leading to two lists labeled "FROM" and "TO"] ### 00:17:31 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three thought bubbles with icons (question mark, eye, person with lightbulb) above two bulleted lists labeled "FROM:" and "TO:", illustrating a conceptual transition.] ### 00:17:47 ## Classifying in context If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? Interview, analyse, and annotate in context How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model ### 00:18:08 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected concepts at the top, and two lists labeled "FROM" and "TO" at the bottom with an arrow indicating progression.] ### 00:18:31 # Classifying in context If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? Interview, analyse, and annotate in context How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Conceptual diagram illustrating a process or transformation, with three interconnected thought bubbles at the top, each containing an icon (question mark, eye, person's head), and two dotted boxes below labeled "FROM" and "TO" listing bullet points, connected by a dotted arrow.] ### 00:18:48 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected conceptual boxes, each with an icon: a question mark, an eye, and a person with a lightbulb] [A dotted line connecting a "FROM" list to a "TO" list] ### 00:18:52 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected thought bubbles with icons (question mark, eye, person with lightbulb) above corresponding text boxes] [Two lists, "FROM" and "TO", connected by a dotted arrow] ### 00:19:14 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected thought bubbles at the top, each with an icon (question mark, eye, person with lightbulb), and two bulleted lists below labeled "FROM" and "TO", indicating a transition.] ### 00:19:37 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? **FROM:** - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management **TO:** - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected concepts: a question, an action, and another question, leading to a comparison of "FROM" and "TO" states.] ### 00:20:02 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected thought bubbles with icons (question mark, eye, person) at the top, and two lists labeled "FROM" and "TO" at the bottom, connected by a dotted line.] ### 00:20:27 # Classifying in context - If time is not a determining factor for engagement or outcomes, what matters when it comes to impact? - Interview, analyse, and annotate in context - How can classifications introduce **strengths based language** into data models? ### FROM: - Generic and binary - No explicit or shared logics - Discretionary - No in-platform management ### TO: - Shared typology and taxonomy - Representation of complexity - In-app classifiers - Assistive decision making for Peer Workers - Outcomes based structured data model [Diagram showing three interconnected thought bubbles with icons (question mark, eye, person with lightbulb) above two lists labeled "FROM" and "TO"] ### 00:20:48 # What do young people want to talk about, and when? - Relationships - Emotional distress - Study & work - Abuse, trauma & harassment - Help seeking [Line graph showing the frequency of different topics young people want to talk about over the days of the week (Monday to Friday)] ### 00:21:10 # What do young people want to talk about, and when? [Line graph showing the frequency of different topics young people want to talk about over the days of the week (Monday to Friday). Topics include Relationships, Emotional distress, Study & work, Abuse, trauma & harassment, and Help seeking.] ### 00:21:31 # What do young people want to talk about, and when? - Relationships - Emotional distress - Study & work - Abuse, trauma & harassment - Help seeking [Line graph showing the frequency of different topics young people want to talk about over the days of the week (Monday to Friday)] ### 00:21:48 # What do young people want to talk about, and when? [Line graph showing the frequency of different topics young people want to talk about over the days of the week (Monday to Friday). Topics include Relationships, Emotional distress, Study & work, Abuse, trauma & harassment, and Help seeking.] ### 00:21:51 # What do young people want to talk about, and when? [Line graph showing the frequency of different topics young people want to talk about over the days of the week (Monday to Friday). Topics include Relationships, Emotional distress, Study & work, Abuse, trauma & harassment, and Help seeking.] ### 00:22:12 # What do young people want to talk about, and when? [Line graph showing the frequency of different topics (Relationships, Emotional distress, Study & work, Abuse, trauma & harassment, Help seeking) over the days of the week (Monday to Friday)] ### 00:22:33 ### Challenge 2: What would have to be true for PeerChat on Demand to be a **safe** service for young people? [Abstract illustration of various overlapping purple rounded shapes] ### 00:22:48 # How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - AI summaries with multiple PII redaction [Image of a person holding a glowing smartphone] [Screenshot of a one-time password entry screen with fields for a 6-digit code and "Verifying" text] [Illustration of a yellow padlock icon] ### 00:22:53 # How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - AI summaries with multiple PII redaction ### Screenshot of "Enter your one time password" UI [Screenshot of a mobile phone displaying a one-time password entry screen with fields for a 6-digit code] [Illustration of a yellow padlock on a purple background] ### 00:23:16 # How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - AI summaries with multiple PII redaction [Screenshot of a mobile phone displaying a one-time password input screen with the text "Enter your one time password. We've sent you an SMS with your 6 digit password. It is valid for 5 minutes." and input fields showing "1 3 3 0 5 7" followed by "Verifying"] [Illustration of a yellow padlock icon on a purple background] ### 00:23:39 # How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - AI summaries with multiple PII redaction [Image of a person holding a smartphone with a glowing screen] [Screenshot of a mobile UI for entering a 6-digit one-time password, showing "1 3 3 0 5 7" and "Verifying"] [Illustration of a yellow padlock icon] ### 00:24:02 ## How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - AI summaries with multiple PII redaction [Image of a person holding a glowing smartphone] [Screenshot of a one-time password entry screen with digits 133057 and "Verifying" text] [Illustration of a yellow padlock icon] ### 00:24:24 # How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - AI summaries with multiple PII redaction [Image of a person holding a phone with a glowing screen] [Screenshot of a mobile app screen for entering a one-time password, showing input fields and "Verifying" text] [Icon of a yellow padlock] ### 00:24:46 # How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - AI summaries with multiple PII redaction [Image of a person holding a smartphone with a glowing screen] [Screenshot of a mobile app showing a one-time password input field with the text "Enter your one time password" and "We've sent you an SMS with your 6 digit password. It is valid for 5 minutes." and a 6-digit code "1 3 3 0 5 7" with "Verifying" below it.] [Illustration of a yellow padlock icon on a purple background] ### 00:25:08 # How does this look in practice? - Trauma informed, recovery oriented language - Increasing the safety of PeerChat through OTP - Enter your one time password. We've sent you ### 00:25:32 ### Challenge 3: Reducing the care gap: HMW recommend the right service at the right time? [Abstract illustration of various purple rounded shapes] ### 00:25:47 ### Challenge 3: Reducing the care gap: HMW recommend the right service at the right time? [Abstract illustration of various purple rounded shapes and circles] ### 00:25:51 ### Challenge 3: Reducing the care gap: HMW recommend the right service at the right time? [Abstract illustration of purple rounded shapes] ### 00:26:14 ### Challenge 3: Reducing the care gap: HMW recommend the right service at the right time? [Abstract arrangement of purple rounded shapes and circles on the right side of the slide, with a small "R" logo and speech bubble icon in the bottom right corner.] ### 00:26:34 ### Challenge 3: Reducing the care gap: HMW recommend the right service at the right time? [Abstract illustration of various overlapping purple circles and rounded rectangles] ### 00:26:47 ### Challenge 3: Reducing the care gap: HMW recommend the right service at the right time? [Abstract illustration of various purple rounded shapes and circles] ### 00:27:08 ## When offboarding is connecting a young person to another service - Need and readiness levels - Confidence building - Expectation setting [Three cards illustrating concepts: one with a sad emoji for "Need and readiness levels", one with a happy emoji hugging a heart for "Confidence building", and one with a calm emoji for "Expectation setting"] ### 00:27:30 # When offboarding is connecting a young person to another service - Need and readiness levels - Confidence building - Expectation setting [Three square cards with rounded corners, each containing a circular emoji-like icon and text below. The first card is pink with a sad face emoji. The second is blue with a happy face emoji hugging a red heart. The third is light green with a content/peaceful face emoji.] ### 00:27:49 # When offboarding is connecting a young person to another service - Need and readiness levels - Confidence building - Expectation setting [Three square cards with rounded corners, each containing a circular emoji. The first card is pink with a sad yellow emoji. The second card is blue with a happy yellow emoji hugging a red heart. The third card is green with a content yellow emoji with closed eyes.] ### 00:27:50 # When offboarding is connecting a young person to another service - Need and readiness levels - Confidence building - Expectation setting [Three square cards with rounded corners, each containing a circular emoji. The first card is pink with a sad yellow emoji. The second card is blue with a happy yellow emoji hugging a red heart. The third card is green with a content yellow emoji with closed eyes.] ### 00:28:11 ### When offboarding is connecting a young person to another service - Need and readiness levels - Confidence building - Expectation setting [Three cards, each with an emoji-like icon: a sad face, a happy face hugging a heart, and a peaceful face.] ### 00:28:32 # When offboarding is connecting a young person to another service - Need and readiness levels - Confidence building - Expectation setting [Three square panels, each with an emoji-like icon: a sad face, a happy face hugging a heart, and a peaceful face with closed eyes.] ### 00:28:47 # Impact [Abstract illustration of several white rounded shapes on a yellow background] ### 00:28:52 # Impact [Abstract illustration of several white rounded shapes on a yellow background] ### 00:29:13 ### What do young people say about the service? 87% felt better 97% felt heard and understood 92% Rated experience as 4 or 5 (Scale 1-5) [Three images of young people, each paired with a statistic about their experience with the service] ### 00:29:35 # What do young people say about the service? - **87%** felt better - **97%** felt heard and understood - **92%** Rated experience as 4 or 5 (Scale 1-5) [Three images of young people, each associated with a data point]