Quality of palliative and end-of-life care: a qualitative study of experts’ recommendations to improve indicators in Quebec (Canada)
Bibliographic record
Abstract
BACKGROUND: In 2021, the National Institute of Public Health (INSPQ) (Quebec, Canada), published an update of the palliative and end-of-life care (PEoLC) indicators. Using these updated indicators, this qualitative study aimed to explore the point of view of PEoLC experts on how to improve access and quality of care as well as policies surrounding end-of-life care. METHODS: Semi-directed interviews were conducted with palliative care and policy experts, who were asked to share their interpretations on the updated indicators and their recommendations to improve PEoLC. A thematic analysis method was used. RESULTS: The results highlight two categories of interpretations and recommendations pertaining to: (1) data and indicators and (2) clinical and organizational practice. Participants highlight the lack of reliability and quality of the data and indicators used by political and clinical stakeholders in evaluating PEoLC. To improve data and indicators, they recommend: improving the rigour and quality of collected data, assessing death percentages in all healthcare settings, promoting research on quality of care, comparing data to EOL care directives, assessing use of services in EOL, and creating an observatory on PEoLC. Participants also identified barriers and disparities in accessing PEoLC as well as inconsistency in quality of care. To improve PEoLC, they recommend: early identification of palliative care patients, improving training for all healthcare professionals, optimizing professional practice, integrating interdisciplinary teams, and developing awareness on access disparities. CONCLUSIONS: Results show that PEoLC is an important aspect of public health. Recommendations issued are relevant to improve PEoLC in and outside Quebec.
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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 | 0.001 | 0.001 |
| 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".