The Technical Feasibility of Integrating Primary Eye Care Into Primary Health Care Systems in Nigeria: Protocol for a Mixed Methods Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Approximately 90% of the 253 million blind or visually impaired people worldwide live in low- and middle-income countries. Lack of access to eye care is why most people remain or become blind. The World Health Organization Regional Office for Africa (WHO-AFRO) recently launched a primary eye care (PEC) package for sub-Saharan Africa-the WHO-AFRO PEC package-for integration into the health system at the primary health care (PHC) level. This has the potential to increase access to eye care, but feasibility studies are needed to determine the extent to which the health system has the capacity to deliver the package in PHC facilities. OBJECTIVE: Our objective is to assess the technical feasibility of integrating the WHO-AFRO PEC package in PHC facilities in Nigeria. METHODS: This study has several components, which include (1) a literature review of PEC in sub-Saharan Africa, (2) a Delphi exercise to reach consensus among experts regarding the technical complexity of the WHO-AFRO PEC package and the capacities needed to deliver it in PHC facilities, (3) development of PEC technical capacity assessment tools, and (4) data collection, including facility surveys and semistructured interviews with PHC staff and their supervisors and village health workers to determine the capacities available to deliver PEC in PHC facilities. Analysis will identify opportunities and the capacity gaps that need to be addressed to deliver PEC. RESULTS: Consensus was reached among experts regarding the technical complexity of the WHO-AFRO PEC package and the capacities needed to deliver it as part of PHC. Quantitative tools (ie, structured questionnaires, in-depth interviews, and observation checklists) and topic guides based on agreed-upon technical capacities have been developed and relevant stakeholders have been identified. Surveys in 48 PHC facilities and interviews with health professionals and supervisors have been undertaken. Capacity gaps are being analyzed. CONCLUSIONS: This study will determine the capacity of PHC centers to deliver the WHO-AFRO PEC package as an integral part of the health system in Nigeria, with identification of capacity gaps. Although capacity assessments have to be context specific, the tools and findings will assist policy makers and health planners in Nigeria and similar settings, who are considering implementing the package, in making informed choices. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17263.
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 | 0.102 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".