Identifying barriers to the production and use of routine health information in Western Province, Zambia
Bibliographic record
Abstract
Recent decades of improvements to routine health information systems in low- and middle-income countries (LMICs) have increased the volume of health data collected. However, countries continue to face several challenges with quality production and use of information for decision-making at sub-national levels, limiting the value of health information for policy, planning and research. Improving the quality of data production and information use is thus a priority in many LMICs to improve decision-making and health outcomes. This qualitative study identified the challenges of producing and using routine health information in Western Province, Zambia. We analysed the interview responses from 37 health and social sector professionals at the national, provincial, district and facility levels to understand the barriers to using data from the Zambian health management information system (HMIS). Respondents raised several challenges that we categorized into four themes: governance and health system organization, geographic barriers, technical and procedural barriers, and challenges with human resource capacity and staff training. Staff at the facility and district levels were arguably the most impacted by these barriers as they are responsible for much of the labour to collect and report routine data. However, facility and district staff had the least authority and ability to mitigate the barriers to data production and information use. Expectations for information use should therefore be clearly outlined for each level of the health system. Further research is needed to understand to what extent the available HMIS data address the needs and purposes of the staff at facilities and districts.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".