Continuing Professional Development for Primary Care Providers in Palliative and End-of-Life Care: A Systematic Review
Bibliographic record
Abstract
Background and Objective: This review updates and expands on previous reviews of educational interventions for primary care providers (PCPs) involved in palliative and end-of-life care (PEoLC) and is the first to include early studies related to medical assistance in dying (MAiD). Methods: A comprehensive search strategy was conducted across five electronic databases to locate published interventional studies related to ongoing PEoLC and/or MAiD education for primary care professionals. A descriptive summary of results and a narrative discussion of common themes and comparisons are provided. Results: Thirty-seven studies met the inclusion criteria. The researchers found a myriad of interventions, including courses based, practical experience, mentoring, and workshops. The researchers categorized results by four domains: attitude, confidence, knowledge, and skills. Across domains, seven educational topics emerged: general care, interprofessional collaboration, nutrition, pain and symptom management, patient communication, and professional coping. Overall, studies employed various methodologies, but often relied on cross-sectionally measured self-assessment. Two articles were found that measured the impact of MAiD education. Conclusion: These findings suggest that PEoLC education can improve PCPs' perceived attitudes, confidence, knowledge, and skills across multiple areas of palliative care practice. While PCPs across studies valued educational interventions, the findings relating to the impact of PEoLC education on PCP's provision of effective PEoLC were unclear. However, most interventions resulted in enhanced confidence and knowledge. To date, there are only two studies that have examined MAiD educational programs. There is a need for studies of higher rigor with more emphasis on follow-up to clarify the impact training has on those involved in PEoLC and MAiD.
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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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".