Does Facilitated Palliative Care Education Improve Patient Identification? A Cluster Randomized Controlled Trial of Primary Care Providers in the CAPACITI Training Program
Bibliographic record
Abstract
Background: Primary care providers (PCPs) play a critical role in initiating palliative care but are often unsure how to identify patients for this approach. Community Access to PAlliative Care via Interprofessional Teams Improvement program (CAPACITI) is a virtual education program that provides practical tools and strategies for PCPs to help them operationalize palliative care, complementing clinical skills training. Objectives: To assess the impact of facilitated versus self-directed versions of CAPACITI, in assisting PCPs to better incorporate an early palliative approach to care in practice. Design: A cluster randomized controlled trial (cRCT). Setting/Subjects: PCP teams across Canada that enrolled in CAPACITI were randomized and stratified by team size and geography. The control group (self-directed) received access to the CAPACITI online session materials (e.g., slide decks, tools, summary sheets, assignments). In addition to this, the intervention group (facilitated) was also invited to participate in facilitated biweekly, virtual webinars. Measurements: Difference between the two groups in self-reported identification of patients requiring a palliative approach to care following the intervention (calculated as percentage of caseload in past three months). Results: A total of 185 teams, representing 566 individuals, enrolled in CAPACITI and completed baseline measures. In total, 295 participants reported providing direct patient care; 166 of these participants (33.9%) were lost to follow-up. Prior to CAPACITI, providers in both groups reported identifying a median of 20.0% of their patients as requiring a palliative approach to care. Post-intervention, these median percentages increased for both groups to 33.3% for the facilitated group, and 40.0% for the self-directed group ( p < 0.001 overall), with no significant difference between groups ( p = 0.3, mean effect estimate = −0.04, 95% CI: [−0.12, 0.04]). Conclusions: We found that CAPACITI improved palliative care identification, regardless of whether the online content was facilitated via webinars or self-directed. Further research is required to examine optimal uses of facilitation for skill development.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".