Occupational therapy training on palliative and end-of-life care: Scoping review
Bibliographic record
Abstract
Introduction While occupational therapists promote quality of life enabling occupation, many receive little to no training on palliative and end-of-life care. This study synthesizes relevant literature to find out what is known about training for them on this matter. Methods A scoping review using Arksey and O’Malley’s five-stage framework exploring literature on palliative and end-of-life care training in occupational therapy (French or English; undergraduate or graduate). The first author screened the titles, abstracts, and keywords and selected literature to be read by the teams to extract and include relevant knowledge. Corroborated thematic analysis synthesized the findings. Result Out of the 384 publications initially identified, 25 were included in the review. To be trained in palliative and end-of-life care, occupational therapists have to (a) be exposed to knowledge on specific (b) topics required for competent practice using (c) educational strategies supporting learning about palliative and end-of-life care. Conclusion Whereas introductory-level knowledge of palliative and end-of-life care should be offered to all students, advanced training should allow occupational therapists to master the philosophy of this type of care, deepen the understanding of topics such as being confronted with death, and empower them to advocate for their unique contribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".