Learner : preceptor ratios for practice‐based learning across health disciplines: a systematic review
Bibliographic record
Abstract
CONTEXT: Practice-based learning is a cornerstone of developing clinical and professional competence in health disciplines. Practice-based learning systems have many interacting components, but a key facet is the number of learners per preceptor. Different learner : preceptor ratios may have unique benefits and pose unique challenges for participants. This is the first comprehensive systematic review of the topic. Our research questions were: What are the benefits and challenges of each learner : preceptor ratio in practice-based learning from the perspectives of the learners, preceptors, patients and stakeholder organisations (i.e. the placing and health care delivery organisations)? Are any ratios superior to others with respect to these characteristics and perspectives? METHODS: Qualitative systematic review of published English-language literature since literature database inception, including multiple health disciplines. RESULTS: Seventy-three articles were included in this review. Eight learner : preceptor ratio arrangements were identified involving nursing, physiotherapy, occupational therapy, pharmacy, dietetics, speech and language therapy, and medicine. Each arrangement offers unique benefits and challenges from the perspectives of learners, preceptors, programmes and health care delivery organisations. Patient perspectives were absent. Despite important advantages of each ratio for learners, preceptors and organisations, some of which may be profession specific, the 2 : 1 and 2+ : 2+ learner : preceptor ratios appear to be most likely to successfully balance the needs of all stakeholders. CONCLUSIONS: Regardless of the learner : preceptor ratio chosen for its expected benefits, our results illuminate challenges that can be anticipated and managed. Patient perspectives should be incorporated into future studies of learner : preceptor ratios.
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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 | 0.031 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".