Appraising publicly available online resources to support patients considering decisions about medical assistance in dying in Canada: an environmental scan
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAiD) was legalized in Canada in 2016, with legislation updated in 2021. It is unclear whether resources are available to help patients make this difficult decision; therefore, we sought to identify and quality appraise Canadian MAiD resources for supporting patients making this decision. METHODS: We conducted an environmental scan by searching Canadian websites for online MAiD resources that were published after the 2016 MAiD legislation, patient targeted, publicly accessible and able to inform decisions about MAiD in Canada. We excluded resources that targeted health care professionals or policy-makers, service protocols and personal narratives. Two authors appraised resources using the International Patient Decision Aids Standards (IPDAS) criteria and the Patient Education Materials Assessment Tool (PEMAT) for health literacy. Descriptive analysis was conducted. We defined resources as patient decision aids if 7 IPDAS defining criteria were met, and we rated resources as adequate for understandability or actionability if the PEMAT score was 70% or greater. RESULTS: We identified 80 MAiD resources. As of March 2023, 62 resources (90%) provided eligibility according to the 2021 legislation and 11 did not discuss any eligibility criteria. The median IPDAS score was 3 out of 7; 52% discussed alternative options and none provided benefits or harms. Of 80 resources, 59% were adequate for understandability and 29% were adequate for actionability. INTERPRETATION: Although many resources on MAiD were updated with 2021 legislation, few were adequate to support patients with lower health literacy. There is a need to determine whether a patient decision aid would be appropriate for people in Canada considering MAiD.
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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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".