576-P: Cocreating a Virtual Care Platform That Delivers Mental Health Support to Adults with Type 1 Diabetes (T1D) Living in Rural and Remote Communities
Bibliographic record
Abstract
Introduction: While mental health (MH) is the cornerstone of diabetes care, access to MH support in rural and remote communities in British Columbia (BC) is limited. The purpose of this study was to design REACHOUT, a virtual care platform, to deliver peer-led MH support to adults with T1D in geographically marginalized communities in BC. Methods: The REACHOUT platform was co-created by our T1D Patient Partner Team (n=16) and experts in digital health, biomedical engineering, behavioural science, and rural health. REACHOUT is built as a customizable extension to the Rocket.chat open-source platform, deployed privately on Amazon Web Services servers in Canada; it offers various forms of encryption to maintain data security. We applied an Agile iterative development approach with multi-staged feedback from T1D Patient Partners and key stakeholders in a participatory action framework. Results: The REACHOUT app has three support delivery features: 1) 1-ON-1 SUPPORT delivered by an individual Peer Supporter (PS) self-selected by the participant from a library of PS profiles. Profiles include descriptors such as stage of life, hobbies, and interests, sociodemographic information, diabetes history and treatment etc. Mode, content, and structure of communication are driven entirely by participants’ support preferences and needs; 2) 24/7 COMMUNITY CHAT ROOMoffers “just-in-time” group support and accommodates needs for emotional validation, normalization, and non-judgmental acceptance; 3) VIDEO HUDDLES are monthly PS-led virtual group sessions highlighting a distress or resilience related theme (e.g. workplace bias and discrimination, cooking low-carb, insurance coverage challenges) . Conclusions: Launched in November 2021, the REACHOUT pilot supports 40 participants with 40 PS. Adoption and utilization patterns for each of the support delivery mechanisms are being monitored and will be compared in mid-2022. Disclosure T.S.Tang: None. P.Johal: None. A.Azhar: None. L.Fisher: Consultant; Ascensia Diabetes Care, Research Support; Eli Lilly and Company. W.H.Polonsky: Consultant; Abbott Diabetes, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Intuity Medical, Inc., Provention Bio, Inc., Sanofi. M.Görges: Research Support; Careteam Technologies Inc, Excelar Technologies, IDENTOS Inc, MedStack Inc, SecureKey Technologies Inc, Smile CDR Inc, Thrive Health, Xerus Medical. Funding Juvenile Diabetes Research Foundation (2-SRA-2020-986-S-B)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".