Palliative and end-of-life care in the masters of social work curriculum
Bibliographic record
Abstract
ABSTRACTObjective:There is a shortage of social workers who have palliative care expertise. The aging U.S. population and advances in extending life for seriously ill persons require social workers in a wide range of health care and other settings with specialized palliative care expertise, as well as those with basic competence in palliative care. The objective of the present study was to document course content on palliative care in MSW programs in the United States and Canada. METHOD: A cross-sectional design with an online questionnaire was used. All 248 accredited MSW programs in the United States and 32 programs in Canada were invited to participate. The measures included the characteristics of the courses on palliative care. RESULTS: Of the 105 participating programs, only 10 had courses dedicated to palliative care, 9 of which were part of a specialization/certificate program. Few programs had plans to develop a dedicated course. There were 106 courses in 63 MSW programs with some content on palliative care. The majority of these had <25%, and few had at least 50%, of palliative care content. SIGNIFICANCE OF RESULTS: Curricula are needed for preparing MSW graduates for specialty hospice and palliative care practice and non-specialty practice. While there are practice competencies for specialty practitioners, consensus on a core curriculum for all MSW students would be beneficial. Consensus on basic palliative care knowledge and skills for non-specialty social workers in health care and other settings and subsequent curriculum development are also needed. Innovative ways in which to introduce basic and more specialized content on palliative care into the already-crowded MSW curricula will be needed.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".