Key informants’ perspectives on integrating community health workers into palliative care teams
Bibliographic record
Abstract
Introduction: Disparities in access to palliative care persist, particularly among underserved populations. We elicited recommendations for integrating community health workers (CHWs) into clinical care teams, by exploring perspectives on potential barriers and facilitators, ultimately aiming to facilitate equitable access to palliative care. Materials and Methods: Twenty-five stakeholders were recruited for semi-structured interviews through purposive snowball sampling at three enrollment sites in the USA. Interviews were conducted to understand perspectives on the implementation of a CHW palliative care intervention for African American patients with advanced cancer. After transcription, primary and secondary coding were conducted. Framework analysis was utilized to refine the data, clarify themes, and generate recommendations for integrating CHWs into palliative care teams. Results: Our sample comprised 25 key informants, including 6 palliative care providers, 6 oncologists, 5 cancer center leaders, 2 cancer care navigators, and 6 CHWs. Thematic analysis revealed five domains of recommendations: (1) increasing awareness and understanding of the CHW role, (2) improving communication and collaboration, (3) ensuring access to resources, (4) enhancing CHW training, and (5) ensuring leadership support for integration. Informants shared barriers, facilitators, and recommendations within each domain based on their experiences. Conclusion: Barriers to CHW integration within palliative care teams included limited awareness of the CHW role and inadequate training opportunities, alongside practical and logistical challenges. Conversely, promoting CHW engagement, providing adequate training, and ensuring support from leadership have the potential to aid integration.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".