Do Socioeconomic Factors Influence Knowledge, Attitudes, and Representations of End-of-Life Practices? A Cross-Sectional Study
Bibliographic record
Abstract
ObjectiveAccess to palliative and end-of-life (EOL) care might be influenced by knowledge, attitudes, and representations of these practices. Socioeconomic factors might then affect what people know about EOL care practices, and how they perceive them. This study aims to compare knowledge, attitudes, and representations regarding EOL practices including assisted suicide, medical assistance in dying, and continuous palliative sedation of adults, according to socioeconomic variables.MethodsA cross-sectional community-based questionnaire study featuring two evolving vignettes and five end-of-life practices was conducted in Quebec, Canada. Three sample subgroups were created according to the participants' perceived financial situation and three according to educational attainment. Descriptive analysis was used to compare levels of knowledge, attitudes, and representations between the subgroups.ResultsNine hundred sixty-six (966) people completed the questionnaire. Two hundred and seventy participants (28.7%) had a high school diploma or less, and 42 participants (4.4%) were facing financial hardship. The majority of respondents supported all end-of-life options and the loosening of eligibility requirements for medical assistance in dying. Differences between subgroups were minor. While respondents in socioeconomically disadvantaged subgroups had less knowledge about EOL practices, those with lower educational attainment were more likely to be in favor of medical assistance in dying, and less likely to favor continuous palliative sedation.ConclusionsPeople living with situational social and economic vulnerabilities face multiple barriers in accessing health care. While they may have poorer knowledge about EOL practices, they have a positive attitude towards medical assistance in dying and assisted suicide, and a negative attitude towards continuous palliative sedation. This highlights the need for future research and interventions aimed at empowering this population and enhancing their access to EOL care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".