Exploring family medicine preceptor and resident perceptions of medical assistance in dying and desires for education.
Bibliographic record
Abstract
OBJECTIVE: To examine the perspectives of family medicine preceptors and residents, including their interest and intent to participate in and their knowledge and willingness to teach or learn about medical assistance in dying (MAID). DESIGN: Two anonymous surveys were distributed via e-mail using a Dillman approach to residents and preceptors. Responses were collected between August 23 and November 29, 2016. Data were analyzed using descriptive and inferential statistics. SETTING: The large, 4-site Queen's University family medicine residency program in southeastern Ontario. PARTICIPANTS: A total of 71 preceptors and 62 residents. MAIN OUTCOME MEASURES: Physician and resident knowledge of and experience, comfort, and confidence with MAID; willingness to participate in MAID; perspectives on the effect of MAID on team relationships; and the importance, desired content, and delivery of MAID education. RESULTS: < .001). Most participants from both groups believed it was important to include MAID in the core family medicine residency curriculum and identified specific curriculum content and delivery strategies. CONCLUSION: Family medicine preceptors and residents are willing and want to learn about MAID. Our research demonstrates a need to integrate MAID into the family medicine residency curriculum, with faculty development and continuing professional development for preceptors.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".