Key informants’ perspectives on integrating community health workers into palliative care teams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it