Kenyan palliative care providers’ and leaders’ perceptions of palliative care research needs and support to facilitate rigorous research
Bibliographic record
Abstract
Abstract Background: Palliative care (PC) can reduce symptom distress and improve quality of life for patients and their families experiencing life-threatening illness. While the need for PC in Kenya is high, PC service delivery and research is limited. Qualitative research is needed to explore potential areas for PC research and support needed to enable that research. This insight is critical for informing a national PC research agenda and mobilizing limited resources for conducting rigorous PC research in Kenya. Objectives: To explore perceptions of priority areas for PC research and support needed to facilitate rigorous research from the perspective of Kenyan PC providers and leaders. Methods: Focus groups (FGs) were conducted in November and December of 2018 using a semi-structured interview guide. FGs were audio-recorded, transcribed, and analyzed using a thematic content analysis approach. Results: Three FGs were conducted (n=22 participants). Eleven themes related to PC research emerged, including research on: 1) beliefs about death, disease, and treatment to inform PC; 2) awareness about PC, 3) integration of PC within the health system; 4) understanding caregiver experiences and needs; 5) community health volunteers (CHVs) and volunteer programs; 6) evaluation of costs and benefits of PC; 7) use of complementary and alternative medicine (CAM) in PC; 8) use of advanced diagnostics and extensive treatment in incurable disease; 9) other suggestions for research, 10) populations for whom PC research is needed; and 11) resources for enabling research. Conclusions: Kenyan PC providers and leaders identified key areas requiring increased scientific inquiry and critical resources needed to enable this research. These findings can help to focus future PC research in Kenya and encourage funding agencies to prioritize the issues identified.
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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 | 0.120 | 0.151 |
| 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 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".