Health Care Professional Experiences and Opinions on Depression and Suicide in People With Diabetes
Bibliographic record
Abstract
Objective:People with diabetes have an increased risk of depression, intentional self-injury and self-harm (ISI), and suicide compared with the general population. This study aimed to explore experiences and awareness of health care professionals (HCPs) regarding depression, ISI, and suicide, and understand resource use and needs among HCPs who care for persons with diabetes (PWD).Methods:Health care professionals who see children and/or adults with type 1 diabetes or type 2 diabetes anonymously completed an online survey about their experiences, opinions, barriers, and needs surrounding identification and care of PWD with depression, ISI, and suicide.Results:One hundred twenty-nine HCPs participated. The majority were medical doctors (MDs) or advanced practice providers (APPs). Only a quarter of MDs and APPs felt very comfortable asking about ISI or suicidal ideation (SI), whereas 20% felt they had received appropriate training to support those with ISI or SI. The primary needs reported include more training on how to ask, respond, and support those expressing ISI and SI. Health care professionals reported wanting better access to resources for PWD.Discussion:The HCPs tend to underestimate SI in the diabetes population and rates of training were low. Areas to address include providing education and training to HCPs to improve identification and management of ISI and suicide risk. These data can inform the development of mechanisms to improve discussions of depression and suicide and of resources to help HCPs support PWD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".