The medico‐legal helpline: A content analysis of postgraduate medical trainee advice calls
Bibliographic record
Abstract
CONTEXT: Available literature exploring medical liability and postgraduate medical education consistently posits that postgraduate trainees worry about their exposure to medico-legal liability. This assumption has formed the basis for research and curriculum development. OBJECTIVES: The aim of this study was to describe the encounters that lead physicians-in-training to seek external medico-legal guidance. We sought to provide empirical evidence on trends and themes related to medico-legal advice requests from physicians-in-training. METHODS: Our primary dataset consisted of records of calls from physicians-in-training to the medico-legal helpline of the Canadian Medical Protective Association (CMPA), a national mutual defence organisation providing medico-legal advice and liability protection for over 95% of Canada's physicians. We conducted a trend analysis of the frequency of calls for advice over 10 years from physician-in-training compared with non-trainee physicians. Furthermore, we performed a content analysis of calls made over the most recent 2 years (2016-2017) to elucidate the concerns that led to trainees seeking medico-legal advice. RESULTS: The 10-year trend analysis revealed that the annual growth in the number of physician-in-training advice calls (8.8%) exceeded other CMPA physician groups and was in excess of trainee population growth over the same period. The content analysis identified four core themes: managing confidential information, complex care situations, academic matters and patient safety incidents. CONCLUSIONS: Our findings indicate that trainees are asking questions about their medico-legal liability with increasing frequency. This study contributes new evidence on the issues that lead to trainees seeking help. We believe that understanding trainees' medico-legal advice requests will support medical educators to tailor quality improvement education to learners' needs.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".