Developing and evaluating a SAFER model to screen for diabetes complications among people experiencing homelessness: a pilot study protocol
Bibliographic record
Abstract
BACKGROUND: Diabetes management combined with housing instability intersects, forcing individuals to triage competing needs and critical stressors, such as safety and shelter, with fundamental diabetes self-management tasks like attending healthcare appointments to screen for the complications of diabetes, leaving individuals overwhelmed and overburdened. We aim to address this disjuncture found within our current healthcare delivery system by providing point-of-care screening opportunities in a more patient-centered approach. METHOD: We describe a pilot study of a novel clinical intervention which provides timely, comprehensive, and accessible screening for diabetes complications to people experiencing homelessness. We will assess the reach, effectiveness, adoption, implementation, and maintenance, as per the RE-AIM framework, of a SAFER model of care (i.e., screening for A1C, feet, eyes, and renal function). A trained nurse will provide this screening within a homeless shelter. During these encounters, eligible participants will be screened for microvascular complications (neuropathy, nephropathy, retinopathy) and have their A1C measured, all at the point of care, using bedside tools and novel technology. Effectiveness, our primary objective, will be evaluated using a pre-post design, by comparing the rate of completion of full microvascular screening during the study period with individuals' own historical screening in the 2-year period prior to enrollment. The other domains of the RE-AIM framework will be assessed using process data, chart reviews, patient surveys, and qualitative semi-structured interviews with service providers and participants. This study will be conducted in a large inner-city homeless shelter within a major urban Canadian city (Calgary, Canada). DISCUSSION: Currently, screening for diabetes complications is often inaccessible for individuals experiencing homelessness, which places heavy burdens on individuals and, ultimately, on already strained emergency and acute care services when complications go undetected at earlier stages. The SAFER intervention will modify the current standard of care for this population in a way that is less fragmented, more person-focused, and timely, with the goal of ultimately improving the rate of screening in an acceptable fashion to identify those requiring specialist referral at earlier stages.
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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 | 0.048 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".