Intimidation or harassment among family medicine residents in Saskatchewan: a cross-sectional survey
Bibliographic record
Abstract
Introduction: Up to 98% of practicing family physicians, and over 75% of resident physicians in Canada experience abusive incidents. Despite the negative consequences of abusive incidents, few residents report these events to their supervisors or institution. We sought to estimate the prevalence of abusive incidents experienced or witnessed by Saskatchewan family medicine residents (FMRs) and identify their responses to these events. Methods: Anonymous survey invitations were emailed to all 110 Saskatchewan FMRs in Saskatchewan in November and December 2020. Demographic characteristics, frequency of witnessed and experienced abusive incidents, sources of incidents and residents' responses were collected. Incidents were classified as minor, major, severe, or as racial discrimination based on a previously published classification system. Results: The response rate was 34.5% (38/110). Ninety-two percent (35/38) of residents witnessed a minor incident and 91.7% (32/36) of residents experienced a minor incident. Seventy-one percent (27/38) of residents witnessed racial discrimination while 19.4% (7/36) of residents experienced racial discrimination. Patients were the most common source of abusive incidents. Twenty-nine percent of residents reported abusive incidents to their supervisors. Most residents were aware of institutional reporting policies. Conclusions: Most Saskatchewan FMRs experienced or witnessed abusive incidents, but few were reported. This study provided the opportunity to reassess policies on abusive incidents, which should consider sources of abuse, confidence in reporting, and education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".