Information needs of francophone health care professionals and the public with regard to medical assistance in dying in Quebec: a qualitative study
Bibliographic record
Abstract
BACKGROUND: In 2016, the Canadian government legalized medical assistance in dying (MAiD) for adults with terminal illness. The objective of this study was to explore the information needs of health care professionals and members of the public regarding MAiD. METHODS: This was a qualitative study involving a 1-day face-to-face forum followed by a 3-week online forum across the province of Quebec conducted in June 2016. French-speaking participants targeted for the study included members of the public (citizens, patients and caregivers) and health care professionals. Participants were recruited through calls for applications to a patient partner network and via social media, and through mailing lists of partner professional and community organizations across Quebec. We used a purposeful sampling strategy to recruit a diverse group of participants. In the forums, deliberations were prompted by short informational videos about MAiD. We performed a thematic analysis to identify key information needs. RESULTS: Fifty members of the public and 35 health care professionals participated. Forty-three people participated in the face-to-face meeting, and 42 people participated online. Participants identified 32 information needs (22 expressed by both members of the public and health care professionals, and 10 specific to members of the public) regarding the definition of MAiD, eligibility criteria, and documenting and evaluating practices. Information needs varied along different stages of the patient's journey. Participants expressed the need to be informed about issues that go beyond the medical and legal aspects of MAiD (e.g., relational, symbolic, psychological and spiritual aspects). INTERPRETATION: The findings show that health care professionals and members of the public have common information needs regarding MAiD and seek information on the relational, emotional and symbolic aspects of this practice. These findings call for concerted efforts to build a common information base - covering dimensions that go beyond the medical and legal aspects of MAiD - to facilitate informed conversations among patients, health care professionals and members of the public.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".