Implementing a Two-Tiered Model of Optometry Training in Mozambique as an Eye Health Development Strategy
Bibliographic record
Abstract
BACKGROUND: The global burden of vision impairment has been acknowledged by the World Health Organisation as a public health challenge. In order to scale up the production of eye health personnel in developing countries, a tiered model of optometry training was explored in Mozambique. OBJECTIVES: The Mozambique case study was evaluated to assess the feasibility of a tiered model of optometry training as a developmental eye health strategy. METHODS: A qualitative, case study approach was used. Semi-structured key informant interviews were conducted and project documents were reviewed in the data collection phase. Data underwent a process of content analysis, using a constant comparative approach across sources, and was analysed thematically using inductive reasoning. RESULTS: Three key themes which emerged were Rationale for a training model, Implementation considerations and Development practice considerations. Results demonstrated that while tiered models of training may have developmental rationale, awareness of the profession and its place in addressing health needs, intensive consultation with local stakeholders and a thorough situational analysis are required for this strategy to be feasible. CONCLUSIONS: A tiered model of training appears to have theoretical basis as a developmental eye health strategy. However, local applicability and legislative alignment is required in order for these training initiatives to be sustainably implemented.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".