“I Am Okay With It, But I Am Not Going to Do It”: The Exogenous Factors Influencing Non-Participation in Medical Assistance in Dying
Bibliographic record
Abstract
Medical assistance in dying (MAID) processes are complex, shaped by legislated directives, and influenced by the discourse regarding its emergence as an end-of-life care option. Physicians and nurse practitioners (NPs) are essential in determining the patient’s eligibility and conducting MAID provisions. This research explored the exogenous factors influencing physicians’ and NPs’ non-participation in formal MAID processes. Using an interpretive description methodology, we interviewed 17 physicians and 18 NPs in Saskatchewan, Canada, who identified as non-participators in MAID. The non-participation factors were related to (a) the health care system they work within, (b) the communities where they live, (c) their current practice context, (d) how their participation choices were visible to others, (e) the risks of participation to themselves and others, (f) time factors, (g) the impact of participation on the patient’s family, and (h) patient–HCP relationship, and contextual factors. Practice considerations to support the evolving social contact of care were identified.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".