Drugs for some but not all: inequity within community health worker teams during introduction of integrated community case management
Bibliographic record
Abstract
BACKGROUND: The Ugandan health system now supports integrated community case management (iCCM) by community health workers (CHWs) to treat young children ill with fever, presumed pneumonia, and diarrhea. During an iCCM pilot intervention study in southwest Uganda, two CHWs were selected from existing village teams of two to seven CHWs, to be trained in iCCM. Therefore, some villages had both 'basic CHWs' who were trained in standard health promotion and 'iCCM CHWs' who were trained in the iCCM intervention. A qualitative study was conducted to investigate how providing training, materials, and support for iCCM to some CHWs and not others in a CHW team impacts team functioning and CHW motivation. METHODS: In 2012, iCCM was implemented in Kyabugimbi sub-county of Bushenyi District in Uganda. Following seven months of iCCM intervention, focus group discussions and key informant interviews were conducted alongside other end line tools as part of a post-iCCM intervention study. Study participants were community leaders, caregivers of young children, and the CHWs themselves ('basic' and 'iCCM'). Qualitative content analysis was used to identify prominent themes from the transcribed data. RESULTS: The five main themes observed were: motivation and self-esteem; selection, training, and tools; community perceptions and rumours; social status and equity; and cooperation and team dynamics. 'Basic CHWs' reported feeling hurt and overshadowed by 'iCCM CHWs' and reported reduced self-esteem and motivation. iCCM training and tools were perceived to be a significant advantage, which fueled feelings of segregation. CHW cooperation and team dynamics varied from area to area, although there was an overall discord amongst CHWs regarding inequity in iCCM participation. Despite this discord, reasonable personal and working relationships within teams were retained. CONCLUSIONS: Training and supporting only some CHWs within village teams unexpectedly and negatively impacted CHW motivation for 'basic CHWs', but not necessarily team functioning. A potential consequence might be reduced CHW productivity and increased attrition. CHW programmers should consider minimizing segregation when introducing new program opportunities through providing equal opportunities to participate and receive incentives, while seeking means to improve communication, CHW solidarity, and motivation.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".