A critical review of psychological safety in clinical learning environments
Bibliographic record
Abstract
BACKGROUND: In recent years the perceived need to ensure the psychological safety of learners has become an imperative. A range of conceptualisations appear to be in use, which could create confusion among medical educators and undermine the learning outcomes psychological safety is meant to improve. METHODS: In this critical literature review, we investigated how the concept of psychological safety is used, identifying the various appeals to the safety of learners in clinical environments. The search was conducted spanning January 2019 to January 2025 with the term 'safety' as pertains to learners then narrowed to 'psychological safety'. We used Bacchi's 'What's the problem represented to be' analytic method. FINDINGS: We analysed 46 papers and identified four functions that psychological safety served: (1) as a solution to a wide range of problems, (2) an achievable goal, (3) a way to manage hierarchical power, and (4) a substitute for attending to emotions. We critique each, demonstrating how psychological safety is often treated as a state that individuals can achieve. CONCLUSIONS: This review offers a reframing of safety, as an ongoing, negotiated process among all those involved rather than an endpoint. This focus on psychological safety as a process could guide practitioners, learners and researchers to contribute to more inclusive learning environments.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".