Building Palliative Care Capacity for Generalist Providers in the Community: Results From the Capaciti Pilot Education Program
Bibliographic record
Abstract
Objective: Primary care providers play an important role in providing early palliative care, however they often lack practical supports to operationalize this approach in practice. CAPACITI is a virtual training program aimed at providing practical tips, strategies, and action plans to help primary care providers offer an early palliative approach to care. The CAPACITI pilot program consisted of 10 facilitated, monthly training sessions, covering identification and assessment, communication, and engaging caregivers and specialists. We present the findings of an evaluation of the pilot program. Method: We conducted a single cohort study of primary care providers who participated in CAPACITI. Study outcomes were the change in the percentage of caseload reported as requiring palliative care and improved confidence in competencies measured on a 20-item, study-created survey. Pre and post survey data were analyzed using paired t-tests. Results: Twenty-two teams representing 127 care providers (including 36 physicians and 28 Nurse Practitioners) completed CAPACITI. Paired comparisons showed a moderate improvement in confidence across the competencies covered (.6 to 1.3 mean improvement across items using seven-point scales, all P < .05). Pre-CAPACITI, clinician prescribers ( N = 32) identified a mean of 1.2% of their caseload requiring a palliative approach to care, which increased to 1.6% post-program ( P = .02). Said differently, the total group of paired clinician prescribers identified 338 patients as requiring palliative care in their caseloads at baseline vs 482 patients following the intervention, for an overall increase of 144 patients in their collective caseloads. Conclusion: CAPACITI improved self-assessed palliative care identification and provider confidence in core competencies. The program demonstrated potential for building palliative care capacity in primary care teams.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".