Understanding how and why upskilling programmes for unregulated care providers can support health equity in underserved communities: a realist review protocol
Bibliographic record
Abstract
INTRODUCTION: Foot ulcers are one of the most devastating complications of diabetes mellitus leading to leg amputations. In Canada, systematically marginalised and racialised populations are more prone to developing foot ulcers and at higher risk of limb amputations. Shortages of regulated healthcare have hindered efforts to provide foot care. Upskilling unregulated care providers (UCPs) to deliver foot screening seems a reasonable solution to reduce limb loss. UCPs can advocate for health equity and deliver appropriate care. There is a need, however, to understand how and why an educational intervention for UCPs providing foot screening for these high-risk groups may or may not work. METHODS AND ANALYSIS: This realist review will follow the Realist And Meta-narrative Evidence Syntheses: Evolving Standards standards. First, we will develop an initial programme theory (PrT) based on exploratory searches and discussions with experts and stakeholders. Then, we will search MEDLINE, Embase, PsycINFO, ERIC, CINAHL and Scopus databases along with relevant sources of grey literature. The retrieved articles will be screened for studies focusing on planned educational interventions for UCPs related to diabetic foot assessment. Data regarding contexts, mechanisms and outcomes will be extracted and analysed using a realist analysis through an iterative process that includes data reviewing and consultation with our team. Finally, we will use these results to modify the initial PrT. ETHICS AND DISSEMINATION: Ethical approval is not required for this review. The main output of this research will be an evidence-based PrT for upskilling programmes for UCPs. We will share our final PrT using text, tables and infographics to summarise our results and draw insights across papers/reports. For academic, clinical, social care and educational audiences, we will produce peer-reviewed journal articles, including those detailing the process and findings of the realist review and establishing our suggestions for effective upskilling programmes. 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 | 0.183 | 0.197 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".