Serious safety events as a window into clinical learning environment dynamics: A qualitative situational analysis
Bibliographic record
Abstract
INTRODUCTION: Clinical learning environments (CLE) are complex and have not been thoroughly explored from the perspective of advancing conceptual understanding of their unique dynamics. An opportunity to advance this understanding rests in examining specific situations, such as what happens when a student/trainee has been involved in a serious patient safety event. METHODS: Shaped by concepts of negotiated orders and discourses, we conducted a qualitative, interpretive study in a large urban university and affiliated health science centre in Canada using document analysis and semi-structured interviews. Documents and interview transcripts were analysed using concepts and tools from Adele Clarke's situational analysis. RESULTS: Between March 2022 and April 2023, we conducted 17 interviews with staff physicians (n = 6), medical residents (n = 2), safety leaders and/or university administrators (n = 9). Analysis revealed counter-vailing forces that must be constantly interpreted, negotiated and re-negotiated by participants attempting to deliver on the aspirations of the CLE. Furthermore, analysis revealed potentially competing discourses about the nature of learning in the CLE, animating long-standing tensions about the role of the CLE in developing clinical expertise and professional identity. DISCUSSION: Our study reveals counter-vailing forces, interacting policies and potential disagreements about the learning imperatives and priorities of the CLE. These counter-vailing pressures shape learning about patient safety. More than learning content or process, invested groups must also learn to negotiate risks and responsibilities distributed across multiple social arenas. These distributions are changing. Understanding these dynamics is essential for educators and researchers seeking to positively influence the CLE. Future CLE research should account for the various pressures acting on health service organizations and the possible implications for educational mandates in these spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".