Teamwork in community health committees: a case study in two urban informal settlements
Bibliographic record
Abstract
Abstract Background Community health committees (CHCs) are mechanisms for community participation in decision-making and overseeing health services in several low-and middle-income countries (LMICs). There is little research that examines teamwork and internal team relationships between members of these committees in LMICs. We aimed to assess teamwork and factors that affected teamwork of CHCs in an urban slum setting in Nairobi, Kenya.Methods Using a qualitative case-study design, we explored teamwork of two CHCs based in two urban informal settlements in Nairobi. We used semi-structured interviews (n = 16) to explore the factors that influenced teamwork and triangulated responses using three group discussions (n = 14). We assessed the interpersonal and contextual factors that influenced teamwork using a framework for assessing teamwork of teams involved in delivering community health services.Results Committee members perceived the relationships with each other as trusting and respectful. They had regular interaction with each other as friends, neighbors and lay health workers. CHC members looked to the Community Health Assistants (CHAs) as their supervisor and “boss”, despite CHAs being CHC members themselves. The lay-community members in both CHCs expressed different goals for the committee. Some viewed the committee as informal savings group and community-based organization, while others viewed the committee as a structure for supervising Community Health Volunteers (CHVs). Some members doubled up as both CHVs and CHC members. Complaints of favoritism arose from CHC members who were not CHVs whenever CHC members who were CHVs received stipends after being assigned health promotion tasks in the community. Underlying factors such as influence by elites, power imbalances and capacity strengthening had an influence on teamwork in CHCs.Conclusion In the absence of direction and support from the health system, CHCs morph into groups that prioritize the interests of the members. This redirects the teamwork that would have benefited community health services to other common interests of the team. Teamwork can be harnessed by strengthening the capacity of CHC members, CHAs, and health managers in team building and incorporating content on teamwork in the curriculum for training CHCs.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| 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".