Learner Experiences Matter in Interprofessional Palliative Care Education: A Mixed Methods Study
Bibliographic record
Abstract
Context Interprofessional collaboration is needed in palliative care and many other areas in health care. Pallium Canada's two-day interprofessional Learning Essential Approaches to Palliative care Core courses aim to equip primary care providers from different professions with core palliative care skills. Objectives Explore the learning experience of learners from different professions who participated in Learning Essential Approaches to Palliative care Core courses from April 2015 to March 2017. Methods This mixed methods study was designed as a secondary analysis of existing data. Learners had completed a standardized course evaluation survey online immediately post-course. The survey explored the learning experience across several domains and consisted of seven closed ended (Likert Scales; 1 = "Total Disagree", 5 = "Totally Agree") and three open-ended questions. Quantitative data were analyzed using descriptive statistics and Kruskal-Wallis non-parametric test tests, and qualitative data underwent thematic analysis. Results During the study period, 244 courses were delivered; 3045 of 4636 participants responded (response rate 66%); physicians (662), nurses (1973), pharmacists (74), social workers (80), and other professions (256). Overall, a large majority of learners (96%) selected "Totally Agree" or "Agree" for the statement "the course was relevant to my practice". A significant difference was noted across profession groups; X 2 (4) = 138; p < 0.001. Post-hoc analysis found the differences to exist between physicians and pharmacists ( X 2 = -4.75; p < 0.001), and physicians and social workers ( X 2 = -6.63; p < 0.001). No significant differences were found between physicians and nurses ( X 2 = 1.31; p = 1.00), and pharmacists and social workers ( X 2 = -1.25; p = 1.00). Similar results were noted for five of the other statements. Conclusion Learners from across profession groups reported this interprofessional course highly across several learning experience parameters, including relevancy for their respective professions. Ongoing curriculum design is needed to fully accommodate the specific learning needs of some of the professions.
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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".