Perspectives on utilization of community based health information systems in Western Kenya
Bibliographic record
Abstract
INTRODUCTION: Health information systems (HIS) are considered fundamental for the efficient delivery of high quality health care. However, a large number of legal and practical constraints influence the design and introduction of such systems. The inability to quantify and analyse situations with credible data and to use data in planning and managing service delivery plagues Africa. Establishing effective information systems and using this data for planning efficient health service delivery is essential to district health systems' performance improvement. Community Health Units in Kenya are central points for community data collection, analysis, dissemination and use. In Kenya, data tend to be collected for reporting purposes and not for decision-making at the point of collection. This paper describes the perspectives of local users on information use in various socio-economic contexts in Kenya. METHODS: Information for this study was gathered through semi-structured interviews. The interviewees were purposefully selected from various community health units and public health facilities in the study area. The data were organized and analysed manually, grouping them into themes and categories. RESULTS: Information needs of the community included service utilization and health status information. Dialogue was the main way of information utilization in the community. However, health systems and personal challenges impeded proper collection and use of information. CONCLUSION: The challenges experienced in health information utilization may be overcome by linkages and coordination between the community and the health facilities. The personal challenges can be remedied using a motivational package that includes training of the Community Health Workers.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".