Public knowledge and attitudes concerning palliative care
Bibliographic record
Abstract
OBJECTIVE: WHO recommends early integration of palliative care alongside usual care to improve quality of life, although misunderstanding of palliative care may impede this. We compared the public's perceived and actual knowledge of palliative care, and examined the relationship of this knowledge to attitudes concerning palliative care. METHODS: We analysed data from a survey of a representative sample of the Canadian public, accessed through a survey panel in May-June 2019. We compared high perceived knowledge ('know what palliative care is and could explain it') with actual knowledge of the WHO definition (knew ≥5/8 components, including that palliative care can be provided early in the illness and together with life-prolonging treatments), and examined their associations with attitudes to palliative care. RESULTS: Of 1518 adult participants residing in Canada, 45% had high perceived knowledge, of whom 46% had high actual knowledge. Participants with high (vs low) perceived knowledge were more likely to associate palliative care with end-of-life care (adjusted OR 2.15 (95% CI 1.66 to 2.79), p<0.0001) and less likely to believe it offered hope (0.62 (95% CI 0.47 to 0.81), p=0.0004). Conversely, participants with high (vs low) actual knowledge were less likely to find palliative care fearful (0.67 (95% CI 0.52 to 0.86), p=0.002) or depressing (0.72 (95% CI 0.56 to 0.93), p=0.01) and more likely to believe it offered hope (1.88 (95% CI 1.46 to 2.43), p<0.0001). CONCLUSIONS: Stigma regarding palliative care may be perpetuated by those who falsely believe they understand its meaning. Public health education is needed to increase knowledge about palliative care, promote its early integration and counter false assumptions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".