Risk factors for childhood illness and death in rural Uttar Pradesh, India: perspectives from the community, community health workers and facility staff
Bibliographic record
Abstract
BACKGROUND: Uttar Pradesh (UP), India continues to have a high burden of mortality among young children despite recent improvement. Therefore, it is vital to understand the risk factors associated with under-five (U5) deaths and episodes of severe illness in order to deliver programs targeted at decreasing mortality among U5 children in UP. However, in rural UP, almost every child has one or more commonly described risk factors, such as low socioeconomic status or undernutrition. Determining how risk factors for childhood illness and death are understood by community members, community health workers and facility staff in rural UP is important so that programs can identify the most vulnerable children. METHODS: This qualitative study was completed in three districts of UP that were part of a larger child health program. Twelve semi-structured interviews and 21 focus group discussions with 182 participants were conducted with community members (mothers and heads of households with U5 children), community health workers (CHWs; Accredited Social Health Activists and Auxiliary Nurse Midwives) and facility staff (medical officers and staff nurses). All interactions were recorded, transcribed and translated into English, coded and clustered by theme for analysis. The data presented are thematic areas that emerged around perceived risk factors for childhood illness and death. RESULTS: There were key differences among the three groups regarding the explanatory perspectives for identified risk factors. Some perspectives were completely divergent, such as why the location of the housing was a risk factor, whereas others were convergent, including the impact of seasonality and certain occupational factors. The classic explanatory risk factors for childhood illness and death identified in household surveys were often perceived as key risk factors by facility staff but not community members. However, overlapping views were frequently expressed by two of the groups with the CHWs bridging the perspectives of the community members and facility staff. CONCLUSION: The bridging views of the CHWs can be leveraged to identify and focus their activities on the most vulnerable children in the communities they serve, link them to facilities when they become ill and drive innovations in program delivery throughout the community-facility continuum.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".