Implementing palliative care education into primary care practice: A qualitative case study of the CAPACITI pilot program
Bibliographic record
Abstract
Abstract Background: CAPACITI is a virtual education program that teaches primary care teams how to provide an early palliative approach to care. After piloting its implementation, we conducted an in-depth qualitative study with CAPACITI participants to assess the effectiveness of the components and to understand the challenges and enablers to virtual palliative care education. Methods: We applied a qualitative case study approach to assess and synthesize three sources of data collected from the teams that participated in CAPACITI: reflection survey data, open text survey data, and focus group transcriptions. We completed a thematic analysis of these responses to gain an understanding of participant experiences with the intervention and its application in practice. Results: The CAPACITI program was completed by 22 primary care teams consisting of 159 participants across Ontario, Canada. Qualitative data was obtained from all teams, including 15 teams that participated in focus groups and 21 teams that provided reflection survey data on CAPACITI content and how it translated into practice. Three major themes arose from cross-analysis of the data: changes in practice derived from involvement in CAPACITI, utility of specific elements of the program, and barriers and challenges to enacting CAPACITI in practice. Participants reported that the multifaceted approach of CAPACITI was helpful in teaching them how to apply a palliative approach to care in practice. Conclusions: Our findings suggest that CAPACITI training increased their identification of patients requiring palliative care, improved communication skills, and enhanced confidence in providing a palliative approach to care. CAPACITI warrants further study on a national scale using a randomized trial methodology.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".