Education in ear and hearing care in remote or resource-constrained environments
Bibliographic record
Abstract
BACKGROUND: At the heart of surgical care needs to be the education and training of staff, particularly in the low-income and/or resource-poor setting. This is the primary means by which self-sufficiency and sustainability will ultimately be achieved. As such, training and education should be integrated into any surgical programme that is undertaken. Numerous resources are available to help provide such a goal, and an open approach to novel, inexpensive training methods is likely to be helpful in this type of setting.The need for appropriately trained audiologists in low-income countries is well recognised and clearly goes beyond providing support for ear surgery. However, where ear surgery is being undertaken, it is vital to have audiology services established in order to correctly assess patients requiring surgery, and to be able to assess and manage outcomes of surgery. The training requirements of the two specialties are therefore intimately linked. OBJECTIVE: This article highlights various methods, resources and considerations, for both otolaryngology and audiology training, which should prove a useful resource to those undertaking and organising such education, and to those staff members receiving it.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".