Task shifting for cataract surgery in eastern Africa: productivity and attrition of non-physician cataract surgeons in Kenya, Malawi and Tanzania
Bibliographic record
Abstract
This project examined the surgical productivity and attrition of non-physician cataract surgeons (NPCSs) in Tanzania, Malawi, and Kenya.Baseline (2008-9) data on training, support, and productivity (annual cataract surgery rate) were collected from officially trained NPCSs using mailed questionnaires followed by telephone interviews. Telephone interviews were used to collect follow-up data annually on productivity and semi-annually on attrition. A detailed telephone interview was conducted if a surgeon left his/her post. Data were entered into and analysed using STATA.Among the 135 NPCSs, 129 were enrolled in the study (Kenya 88, Tanzania 38, and Malawi 3) mean age 42 years; average time since completing training 6.6 years. Employment was in District 44%, Regional 24% or mission/ private 32% hospitals. Small incision cataract surgery was practiced by 38% of the NPCSs. The mean cataract surgery rate was 188/year, median 76 (range 0-1700). For 39 (31%) NPCSs their surgical rate was more than 200/year. Approximately 22% in Kenya and 25% in Tanzania had years where the cataract surgical rate was zero. About 11% of the surgeons had no support staff.High quality training is necessary but not sufficient to result in cataract surgical activity that meets population needs and maintains surgical skill. Needed are supporting institutions and staff, functioning equipment and programs to recruit and transport patients.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".