Do learners implement what they learn? Commitment-to-change following an interprofessional palliative care course
Bibliographic record
Abstract
BACKGROUND: Palliative care educators should incorporate strategies that enhance application into practice by learners. Commitment-to-change is an approach to reinforce learning and encourage application into practice; immediately post-course learners commit to making changes in their practices as a result of participating in the course ("statements") and then several weeks or months later are prompted to reflect on their commitments ("reflections"). AIM: Explore if and how learners implemented into practice what they learned in a palliative care course, using commitment-to-change reflections. DESIGN: Secondary analysis of post-course commitment statements and 4-months post-course commitment reflections submitted online by learners who participated in Pallium Canada's interprofessional, 2-day, Learning Essential Approaches to Palliative Care (LEAP) Core courses. SETTING/PARTICIPANTS: Primary care providers from across Canada and different profession who attended LEAP Core courses from 1 April 2015 to 31 March 2017. RESULTS: About 1063 of 4636 learners (22.9%) who participated in the 244 courses delivered during the study period submitted a total of 4250 reflections 4 months post-course. Of these commitments, 3081 (72.5%) were implemented. The most common implemented commitments related to initiating palliative care early across diseases, pain and symptom management, use of clinical instruments, advance care planning, and interprofessional collaboration. Impact extended to patients, services, and colleagues. Barriers to implementation into practice included lack of time, and system-level factors such as lack of support by managers and untrained colleagues. CONCLUSIONS: Examples of benefits to patients, families, services, colleagues, and themselves were described as a result of participating in the courses.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".