Interventions for improved diabetes control and self-management among those experiencing homelessness: protocol for a mixed methods scoping review
Bibliographic record
Abstract
BACKGROUND: Diabetes is a chronic medical condition that requires patients to be actively engaged in intensive self-management to achieve optimal clinical outcomes. Unfortunately, individuals who are experiencing homelessness often struggle to manage diabetes and consequently suffer numerous and severe complications-both acute and chronic. There are many barriers to optimal diabetes self-management among this population, and this may be exacerbated by the lack of tailoring and customization of care to this unique population. Given this disconnect, it is likely that many organizations have attempted to provide specialized innovations for this population-which may or may not be reported in the formal literature. Our objective is to perform a scoping review to summarize and synthesize the experiences of those who have attempted to provide tailored interventions. METHODS: We propose a mixed methods scoping review that will include both a formal search of the published literature (MEDLINE, CINAHL, EMBASE, Web of Science, Scopus) and a thorough search of the grey literature. Eligible articles and documents are those that report on an intervention or guideline for the management of diabetes among those experiencing homelessness. All titles and abstracts will undergo duplicate review, as will the full article/document. We will include any report that either includes a description of an intervention or provides recommendations for the treatment of individuals who are homeless with diabetes. We will extract both qualitative and quantitative data for analysis and interpretation. Meta-analysis will not be performed. DISCUSSION: Those experiencing homelessness who also have diabetes often struggle to manage their chronic condition. When care is tailored to suit their needs, it is feasible that outcomes may be improved. By collating and synthesizing information from diverse organizations and jurisdictions, we hope to facilitate the sharing of knowledge with others who wish to provide this type of care.
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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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".