Leading practices in the development and delivery of case-based learning programmes for health and social care provider education: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: As care needs increase in complexity, a shift to people-centred, integrated care is required to meet the full range of health and social care needs of clients. However, limited opportunities exist for care providers to develop interprofessional competencies as part of pre-licensing and/or continuing education. New learning models, such as case-based learning (CBL), that facilitate the development of interprofessional competencies and are aligned with practice realities of providers are needed. This scoping review will collate and codify leading practices and knowledge gaps in the development and delivery of CBL programmes in pre-licensing and continuing education for health and social care providers. METHODS AND ANALYSIS: A scoping review will be conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. MEDLINE, CINAHL Plus, Scopus, ERIC Institute of Education Sciences, PsycINFO and Education Source will be searched for peer-reviewed literature; Google Scholar, ProQuest Dissertations and Web of Science will be searched for grey literature. Reference lists of full-text scholarly sources, key journals and authors will be searched manually. Study selection and extraction will be conducted by two independent reviewers. English sources published between 2014 and 2024 that discuss CBL epistemologies, characteristics, delivery mechanisms, programme limitations and/or programme evaluation in health and social care pre-licensing and/or professional training will be included. Data will be analysed using directed content analysis and synthesised as a narrative summary. ETHICS AND DISSEMINATION: This scoping review protocol was reviewed by the Southlake Health Research Ethics Board and received ethics exemption. Findings will be disseminated through peer-reviewed publication, conferences, professional networks and social media, and used to inform the development of an evidence-based training programme for health and social care providers. REGISTRATION DETAILS: Open Science Framework https://doi.org/10.17605/OSF.IO/6YXHN.
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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 | 0.182 | 0.118 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.020 |
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".