Situation Analysis of Uncorrected Refractive Errors In Sub-Saharan Francophone African Countries
Bibliographic record
Abstract
Abstract Background: Uncorrected refractive errors (URE) are a serious public health problem by their magnitude, the multiple consequences they result in, but also by the inability of the countries of French-speaking Sub-Saharan Africa (FSSA) to meet the needs of the population. Governance problems, associated with human resources problems, financing problems for care, infrastructure and consumables, led us to initiate this study, the objective of which is to analyze the situation of UREs in FSSA with the stakeholders involved in the eye health system. Materials and Methods: We carried out a cross-sectional survey of eye health actors and stakeholders in all of the French-speaking Sub-Saharan Africa countries from March 1 st to August 31 st , 2020. An online questionnaire was developed and translated into French, and then sent to the targeted eye health stakeholders involved in eye health. The survey and data collection were carried out in two phases: first by collecting information from the eye health officials of the countries which then enabled us to reach all the other actors in the country. Data were entered directly into SPSS 20 software followed by cleaning prior to analysis and presented as percent, mean or median, and standard deviation. Results: A total of 500 questionnaires were sent to the various actors involved in eye health in the 21 countries of French-speaking Sub-Saharan Africa. The number of people who opened the questionnaire is 215, of which 151 have completed at least one question.Eye health policy documents existed in countries according to 95% of respondents. In the words of 76.6%, 54.6% and 85.2% of the participants respectively, the mechanisms for describing the tasks of the agents, for reporting to the actors and for collecting data existed. Also, according to respectively 61.5%, 58.8 % and 61.3% of respondents, the following are not effective: existence of documents of standards and procedures, specific allocation of eye health in the budget of the Ministry of Health and the obligation of continuous training. Conclusion: Although policy documents that comply with standards do exist in the French-speaking region of sub-Saharan Africa, several challenges remain to be taken up; in particular the involvement of all stakeholders of the health system, and the strengthening in the areas of governance, financial and human resources, as well as the information and supply system for materials and consumables, hence the need to initiate more targeted research activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".