Physician experiences with medical assistance in dying
Bibliographic record
Abstract
OBJECTIVE: To explore physician experiences with medical assistance in dying (MAID). DESIGN: An exploratory qualitative research approach using thematic analysis. SETTING: Six communities in northwestern Ontario. PARTICIPANTS: Twenty-three physicians who perform, refer patients for, or are otherwise affected by MAID. METHODS: One semistructured focus group and one-to-one interviews, transcribed and analyzed thematically. MAIN FINDINGS: The legislation of MAID added a layer of complexity to the work of physicians in northwestern Ontario, as MAID requires physician leadership, knowledge, time, and careful implementation. Four themes were identified from the interviews that unpacked this additional layer of complexity: relationships, motivation, time and resources, and getting others on board. CONCLUSION: The logistics of health care delivery in northwestern Ontario communities are complex and layered, which contributes to the burden of physicians as they work to provide MAID.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".