The epidemiology of competence: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Factors that are important to the competence of healthcare providers have important consequences for quality of healthcare. Although some previous research has discussed risks or supports to the competence of clinicians, a thorough exploration is currently lacking. The purpose of this review is to examine the literature examining risks and supports to clinical competence of healthcare practitioners and trainees engaged in field-based education. In this study, field-based education refers to teaching or training in a real-world/work-based setting. METHODS AND ANALYSIS: We will employ an established scoping review methodology. Eligible studies will include those that mention (1) a healthcare professional, (2) competence from field-based education throughout the lifespan/career and (3) a risk or support to competence. Four authors will independently apply the inclusion criteria to all studies, regardless of study design. Our data extraction will include information on study design, location and type of study and we will develop a comprehensive list of risks and supports that are discussed in the literature. ETHICS AND DISSEMINATION: Since this is a review of the literature, ethics approval is not indicated. We will disseminate the findings from this study in publications in peer-reviewed journals as well as presentations at relevant national and international conferences.
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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 | 0.116 | 0.126 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.097 | 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".