Understanding Delay in Accessing Specialist Emergency Eye Care in a Developing Country: Eye Trauma in Tanzania
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To determine the extent and reasons for delay in accessing specialist eye care following a significant eye injury. METHODS: Mixed methods study involving 93 consecutive admissions to Kilimanjaro Christian Medical Center, Tanzania (KCMC). Semi-structured interviews were conducted and supplemented by a review of medical notes. A statistical analysis of delay and predictor variables was conducted. Framework analysis of interviews was conducted. RESULTS: Ninety of 93 patients took part. Significant visual loss was determined in 95.5% of affected eyes on arrival. The mean delay for treatment was 6.8 days. Of participants, 61.1% visited some health facility within 24 hours, and 82.2% within 48 hours. Injury on a weekend, using topical drops and visiting other health facilities before KCMC were independently associated with delay greater than 24 hours and greater than 48 hours, female gender with was associated with delay greater than 24 hours. Patient journeys involved key milestones and processes. Journeys were frequently "circular," involving delays caused by repeated visits to health units unable to treat the injury, often on a health worker's advice. Systems problems included unclear referral systems and opening times, frequent staff absence and unqualified staff deputizing. Individual health workers had an important influence on delay but their performance appeared variable. They influenced patient journeys positively when they made an accurate diagnosis, referred directly to KCMC, discussed practicalities and communicated the seriousness of the injury, the need for urgent treatment and the adverse consequences of delay. CONCLUSIONS: There is significant delay in accessing appropriate specialist care following eye injury in Tanzania, much of which occurs after first visiting a health facility. We present a new model of delay that may help guide interventions to reduce this delay.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it