Research Article Monthly Incidence Rates of Abusive Encounters for Canadian Family Physicians by Patients and Their Families
Bibliographic record
Abstract
Copyright © 2010 Baukje (Bo) Miedema et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. The goal of this study was to examine the monthly incidence rates of abusive encounters for family physicians in Canada. Methods. A 7-page cross-sectional survey. Results. Of the entire study sample (N = 720), 29 % of the physicians reported having experienced an abusive event in the last month by a patient or patient family member. Abusive incidents were classified as minor, major, or severe. Of the physician participants who reported having been abused, all reported having experienced a minor event, 26 % a major, and 8 % a severe event. Of the physicians who experienced an abusive event, 55 % were not aware of any policies to protect them, 76 % did not seek help, and 64 % did not report the abusive event. Conclusion. Family physicians are subjected to significant amounts of abuse in their day-to-day practices. Few physicians are aware of workplace policies that could protect them,
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".