Are You Prepared for End-of-Life Care? A Content Review of Canadian End-of-Life Care Standards and Competencies for Saskatchewan
Bibliographic record
Abstract
BACKGROUND: The provision of end-of-life care is receiving attention locally, provincially, and nationally in Canada. It is important to ensure that interprofessional standards and competencies are in place to provide quality end-of-life care that meets the needs of patients and their families. The purpose of this content review was to identify core standards and competencies essential to an interprofessional team providing end-of-life care. METHODS: The researchers conducted a review of health professional associations and registration bodies that support professionals providing end-of-life care to identify existing standards and competencies. Key concepts were reviewed and organized using thematic analysis; relationships were developed; and core themes for interprofessional end-of-life care were identified. RESULTS: Four themes essential to the provision of end-of-life care were common across all health professions: (1) access to care, (2) professional practice, (3) person-centered care, and (4) the process and delivery of care. CONCLUSIONS: Health professional associations need to ensure end-of-life care standards and competencies are in place for the provision of appropriate and holistic care. Aligning standards and competencies across professions improves the preparedness of health professionals to provide interprofessional end-of-life care.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".