Attitudes towards mental health, mental health research and digital interventions by young adults with type 1 diabetes: A qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Young people with type 1 diabetes are at increased risk of mental disorders. Whereas treatment need is high, difficulty recruiting young people with type 1 diabetes into psychosocial studies complicates development, testing and dissemination of these interventions. OBJECTIVE: Interviews with young adults with type 1 diabetes were conducted to examine attitudes towards mental health and mental health research, including barriers and motivators to participation in mental health studies and preferred sources of mental health support. The interviews were audio-taped, transcribed and evaluated via thematic analysis. SETTING AND PARTICIPANTS: Young adults with type 1 diabetes were recruited via social media channels of 3 advocacy organizations. A total of 31 young adults (26 females and 5 males) with an average age of 22 years were interviewed between October 2015 and January 2016. RESULTS: Participants were largely unaware of their increased vulnerability to common mental health problems and knew little about mental health research. Major barriers to participation included perceived stigma and lifestyle issues and low levels of trust in researchers. Opportunities to connect with peers and help others were described as key motivators. Psychological distress was considered normal within the context of diabetes. A need for some level of human contact in receiving psychosocial support was expressed. DISCUSSION AND CONCLUSION: Findings provide valuable insights into the complex dynamics of engaging young adults with type 1 diabetes in mental health studies. Interviewees provided practical suggestions to assist investigation and delivery of psychosocial interventions for this vulnerable group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".