Sexual harassment of female chiropractors by their patients: a pilot survey of faculty at the Canadian Memorial Chiropractic College.
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to survey a group of female chiropractors and inquire as to whether or not they had been sexually harassed by their patients. METHODS: An online questionnaire was emailed via Survey Monkey to 47 female faculty members at the Canadian Memorial Chiropractic College (CMCC). Respondents were asked if they had been sexual harassed and, if so, the characteristics of the incident(s), their response to it, how serious they perceived the problem to be and whether or not they felt prepared to deal with it. RESULTS: Nineteen of 47 questionnaires were completed and returned. Of these 19, eight respondents reported being sexually harassed by a patient (all male), most commonly within the first 5 years of practice and most commonly involving a 'new' patient. It was rarely anticipated. The nature of the harassment varied and respondents often ignored the incident. Most respondents perceive this to be a problem facing female chiropractors. DISCUSSION: Although this is the first survey of its kind, this is a significant problem facing other healthcare professionals. CONCLUSIONS: Among this group of respondents, sexual harassment by patients was a common occurrence. More training on how to handle it, during either a student's chiropractic education or offered as a continuing education program, may be warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".