Barriers and facilitators to medical assistance in dying (MAID) discussions in primary care
Bibliographic record
Abstract
In Canada, medical assistance in dying (MAiD) is an end of life intervention intended to offer increased control to Canadians within the dying process (Brassfield & Buchbinder, 2020). Despite the legalization of MAiD in 2016 and developments in MAiD research, many primary care providers (PCPs) reported feeling reluctant to discuss MAiD with their patients (Otte, Jung, Elger, & Bally, 2017). In Canada, PCPs are typically medical doctors (MDs) or nurse practitioners (NPs) who care for patients in outpatient, non-acute care settings (Statistics Canada, 2019). As limited research has been synthesized on barriers and facilitators of MAiD discussions within the Canadian primary care context, the purpose of this integrative literature review (ILR) was to assess what factors promoted and prevented PCPs from discussing MAiD with their patients. After identifying a research question and related keywords, six databases were searched to identify relevant studies. The initial search yielded 1,874 results, which underwent inclusion and exclusion criteria and resulted in 12 references being included in the review. After collating the data, evaluating it, and summarizing the results, facilitators and barriers that affected the discussion of MAiD between PCPs and patients were divided into intrinsic and interpersonal factors. Intrinsic factors included PCP emotions, values, beliefs, education, and training, while interpersonal factors included communication, relationship, and administrative burdens (Brooks, 2019; Brassfield & Buchbinder, 2020; Selby & Bean, 2019; Kelly & Varghese, 2006; Pasman, Willems, & Onwuteaka-Philipsen, 2013; Hagens, Onwuteaka-Philipsen, & Pasman, 2017). Based on the findings of this review, strategies to promote MAiD discussions between PCPs and patients were outlined. Recommendations included identifying and addressing values, beliefs, and emotions; developing communication skills and strategies; promoting patientii provider relationships; and mitigating administrative burdens (Kelly & Varghese, 2006; Selby & Bean, 2019; Pasman et al., 2013; Brooks, 2019). As MAiD-related research in Canada progresses through its pioneering phase, future research has been suggested to support the development of communication guides specific to MAiD as well as the evaluation of PCP approaches to MAiD discussions within the Canadian primary care setting (Selby & Bean, 2019; Brooks, 2019).
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".