Assessing the Quality of Sick Child Care Provided by Community Health Workers
Bibliographic record
Abstract
BACKGROUND: As community case management of childhood illness expands in low-income countries, there is a need to assess the quality of care provided by community health workers. This study had the following objectives: 1) examine methods of recruitment of sick children for assessment of quality of care, 2) assess the validity of register review (RR) and direct observation only (DO) compared to direct observation with re-examination (DO+RE), and 3) assess the effect of observation on community health worker performance. METHODS: We conducted a survey to assess the quality of care provided by Ethiopian Health Extension Workers (HEWs). The sample of children was obtained through spontaneous consultation, HEW mobilization, or recruitment by the survey team. We assessed patient characteristics by recruitment method. Estimates of indicators of quality of care obtained using RR and DO were compared to gold standard estimates obtained through DO+RE. Sensitivity, specificity, and the area under receiver operator characteristic curve (AUC) were calculated to assess the validity of RR and DO. To assess the Hawthorne effect, we compared estimates from RR for children who were observed by the survey team to estimates from RR for children who were not observed by the survey team. RESULTS: Participants included 137 HEWs and 257 sick children in 103 health posts, plus 544 children from patient registers. Children mobilized by HEWs had the highest proportion of severe illness (27%). Indicators of quality of care from RR and DO had high sensitivity for most indicators, but specificity was low. The AUC for different indicators from RR ranged from 0.47 to 0.76, with only one indicator above 0.75. The AUC of indicators from DO ranged from 0.54 to 1.0, with three indicators above 0.75. The differences between estimates of correct care for observed versus not observed children were small. CONCLUSIONS: Mobilization by HEWs and recruitment by the survey teams were feasible, but potentially biased, methods of obtaining sick children. Register review and DO underestimated performance errors. Our data suggest that being observed had only a small positive effect on the performance of HEWs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".