Validity and reliability of data collected by community health workers in rural and peri-urban contexts in Kenya
Bibliographic record
Abstract
Reliability and validity of measurements are important for the interpretation and generalisation of research findings. Valid, reliable and comparable measures of health status of individuals are critical components of the evidence base for health policy. The need for sound information is especially urgent in the case of emerging diseases and other acute health threats, where rapid awareness, investigation and response can save lives and prevent broader national outbreaks and even global pandemics. Several successfully implemented health interventions have involved community health workers (CHWs) in reaching out to the community, and the Community Health Strategy is one such an intervention. The government of Kenya, through the Ministry of Public Health and Sanitation has rolled out the strategy as a way of improving health care at the household level. It involves CHWs collecting health status data at the household level, which is presented at community meetings in which the community discusses the results, identifies action areas, and plans activities for improving their health status. Ten percent of all households visited by CHWs for data collection in different sites (rural and peri-urban) were systematically selected and visited a second time by technically trained research team members. The test-retest method was applied to establish reliability. The Kappa score was used to measure reliability, while sensitivity, specificity, and positive predictive values were used to measure validity. Inter-observer agreement between the two sets of data in both sites was good; most indicators measured slight agreement. However, some indicators demonstrated greater discrepancies between the two data sets (e.g. measles immunization). Specificity measures were more stable in Butere (rural), which had more than 90% in all the indicators tested, compared to Nyalenda (peri-urban), which fluctuated between 50% and 90%. There were variable reliability results in the peri-urban site for the indicators measured, while the rural site presented more stable results. This is also depicted in the validity measures in both sites. The paper concludes that there are convincing results that CHWs can accurately and reliably collect certain types of community data which has cost-saving implications, especially for resource poor settings.
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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".