Medical abortion and family physicians. Survey of residents and practitioners in two Ontario settings.
Bibliographic record
Abstract
OBJECTIVE: To determine the knowledge, attitudes, and interest in providing medical abortion reported by family physicians and residents in rural and urban settings. DESIGN: A self-administered mailed survey using the modified Dillman method. SETTING: Hamilton and Thunder Bay County in Ontario. PARTICIPANTS: Family medicine residents (n = 93) and physicians (n = 234) in predominantly urban (Hamilton) and rural (Thunder Bay) settings. All faculty family physicians at McMaster University practising general family medicine and all family physicians in Thunder Bay County were surveyed. MAIN OUTCOME MEASURES: Knowledge of, attitudes toward, and interest in providing medical abortion. RESULTS: Overall response rate to the survey was 62.7% (n = 327); 74.2% (69/93) of residents responded; 58.1% (136/234) of physicians responded. Physicians and residents rated their knowledge about medical abortion as poor, but most were interested in receiving more information and training in this area. Many (83.1%, 157/189) reported that medical abortion was an acceptable procedure for family physicians to perform, and 52.0% (64/123) of the physicians would consider providing medical abortions for their patients. Residents training in the more rural Thunder Bay program were less likely to support first-trimester abortions for both medical and nonmedical reasons than those training in Hamilton (P < .05). Male respondents were significantly less supportive of abortion for nonmedical reasons and were less likely to consider providing medical abortions for their patients (P < .05). CONCLUSION: Most family physicians and residents showed interest in receiving more information about and training in medical abortion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".