Moving beyond ‘don't ask, don't tell’: Mental health needs of adults with type 1 diabetes in rural and remote regions of British Columbia
Bibliographic record
Abstract
AIMS: To investigate the mental health needs of adults with type 1 diabetes living in rural and remote regions of Interior, British Columbia (BC) and identify factors associated with accessing support. We also explored perspectives around using peer support and digital health strategies for delivering mental health support. METHODS: This study recruited 38 adults with type 1 diabetes to complete a self-report survey and participate in focus groups. We conducted six 90-min focus groups that addressed the following: current and past mental health needs, social media use for type 1 diabetes support, peer supporter recruitment and training, and support delivery features for virtual care platforms. Focus groups were recorded, transcribed, quality checked, coded and analysed to develop themes and subthemes. RESULTS: Four core themes emerged: (1) emotional challenges linked to type 1 diabetes management, (2) unique type 1 diabetes-related concerns in rural and remote communities, (3) previous support experiences and future support needs and (4) diabetes-related mental health support interventions involving peer support and digital health strategies. Existing support services are inadequate in meeting the needs of type 1 diabetes adults in Interior BC. Some have turned towards social media as a way to connect with the type 1 diabetes community for support. CONCLUSIONS: Though type 1 diabetes adults living in rural and remote settings experience distress associated with the ongoing burdens, frustrations and fears of managing a complex chronic condition, many have not been offered support and do not know how to seek services in the present/future. Peer support and digital health strategies are two potential solutions to address this care gap.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".