Emotional impact on healthcare providers involved in medical assistance in dying (MAiD): a systematic review and qualitative meta-synthesis
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAiD) traverses challenging and emotionally overwhelming territories: healthcare providers (HCPs) across jurisdictions experience myriad of affective responses secondary to possible tensions between normative and interwoven values, such as sanctity of life, dignity in death and dying and duty to care. OBJECTIVE: To determine the emotional impact on HCPs involved in MAiD. METHODS: Inclusion restricted to English language qualitative research studies from four databases (OVID Medline, EMBASE, CINAHL and Scopus), from beginning until 30 April 2021, and grey literature up to August 2021 were searched. Key author, citation and reference searches were undertaken. We excluded studies without rigorous qualitative research methodology. Included studies were critically appraised using the Joanna Briggs Institute's critical appraisal tool. Analysis was conducted using thematic meta-synthesis. The cumulative evidence was assessed for confidence using the Confidence in the Evidence from Reviews of Qualitative Research approach. RESULTS: The search identified 4522 papers. Data from 35 studies (393 physicians, 169 nurses, 53 social workers, 22 allied healthcare professionals) employing diverse qualitative research methodologies from five countries were coded and analysed. The thematic meta-synthesis showed three descriptive emotional themes: (1) polarised emotions including moral distress (n=153), (2) reflective emotions with MAiD as a 'sense-making process' (n=251), and (3) professional value-driven emotions (n=352). DISCUSSION: This research attempts to answer the question, 'what it means at an emotional level', for a MAiD practitioner. Legislation allowing MAiD for terminal illness only influences the emotional impact: MAiD practitioners under this essential criterion experience more polarised emotions, whereas those practising in jurisdictions with greater emphasis on allaying intolerable suffering experience more reflective emotions. MAiD practitioner's professional values and their degree of engagement influence the emotional impact, which may help structure future support networks. English language literature restriction and absence of subgroup analyses limit the generalisability of results.
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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 | 0.055 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".