Identifying Challenges and Solutions to Providing Diabetes Care for Those Experiencing Homelessness
Bibliographic record
Abstract
Introduction: Health care providers face a multitude of challenges in providing care to patients with diabetes who are experiencing homelessness. Considering the unique circumstance faced by this population, mainstream services must be adapted and tailored to meet patients’ needs. The objective of this study was to explore both the barriers faced by providers and programs in offering comprehensive diabetes care to these patients, and their suggested areas for improvement. Methods: We conducted semi-structured interviews with providers who care for patients who have diabetes and/or who experience homelessness. Participants included primary care providers, specialist physicians, dietitians, shelter staff, outreach workers, and diabetes educators in five Canadian centres (n=96). Responses were analyzed using qualitative thematic analysis. Results: Barriers most frequently cited by providers were a lack of resources for staff. Other challenges included policy barriers (restrictions on billing codes, care professionals’ scope of practice, and the structure of financial support for this population), duplication of services, and alternative priorities of care. Participants identified several strategies to improve care, which targeted the following spheres: location of service provision and coordination of care, policy changes, and extending funding and resources for staff, such as augmented funding to hire allied health professionals in outpatient settings and increasing outreach capabilities. Conclusion: Programs that strive to address the unique needs of clients experiencing homelessness face numerous challenges. Unique potential solutions to these barriers, such as service provision in a convenient location involving social and health services, incorporating allied health care providers in care to a greater extent, and updating policies to reflect the social complexity of the population can improve diabetes care.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".