Upskilling programmes for unregulated care providers to provide diabetic foot screening for systematically marginalised populations: how, why and in what contexts do they work? A realist review
Bibliographic record
Abstract
OBJECTIVE: We aimed to understand how, why and in what context upskilling programmes for unregulated care providers (UCPs) to provide foot screening for systematically marginalised groups living with diabetes were implemented. DESIGN: We used realist synthesis based on Realist And Meta-narrative Evidence Syntheses: Evolving Standards guidance. DATA SOURCES: We searched the Medline, Embase, PsycINFO, CINAHL, ERIC, Web of Science Core Collection, and Scopus databases and the grey literature (Google Scholar, ProQuest Dissertations and Theses) up to November 2022. ELIGIBILITY CRITERIA: We included experimental and non-experimental articles in English that either described mechanisms or discussed expected outcomes for educational interventions for patients and family caregivers or healthcare providers, both regulated and unregulated. We also included articles that evaluated the impact of foot care programmes if the UCPs' training was described. DATA EXTRACTION AND SYNTHESIS: The lead author extracted, annotated and coded uploaded relevant data to identify contexts, mechanisms and outcome configurations using MAXQDA (a qualitative data analysis software). We used deductive and inductive coding to structure the process. Our team members double-reviewed and appraised a random sample of 20% of articles at all stages to ensure consistency. RESULTS: Our search identified 52 articles. Evidence suggested the necessity of developing upskilling foot screening programmes within the context of preventive care programmes that also provide education in diabetes, and early referrals for appropriate interventions. Multidisciplinary programmes created an ideal context facilitating coordination between UCPs and their regulated counterparts. Engaging patients and community partners, using a competency-based model, and incorporating cultural competencies were determinants of success for these programmes. CONCLUSION: This review provides a realistic programme theory for the mechanisms used, the context in which these programmes were developed, and the expected outcomes to train UCPs to provide preventive foot care for systematically marginalised populations. PROSPERO REGISTRATION NUMBER: CRD42022369208.
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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 | 0.055 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".