An e-health needs assessment of medical residents in Cameroon
Bibliographic record
Abstract
Medical residents from Yaounde I University in Cameroon are required to spend periods of time in rural or remote locations to complete their training. To determine if e-health might lessen their isolation and enhance patient care, a needs assessment of the residents was performed using a brief questionnaire (five items) about the situation in which residents found themselves outside their medical school environment. We gave the questionnaires to 45 residents. Seventeen questionnaires had been returned at the time of the site visit, a response rate of 38%. Most residents indicated that the ability to contact a mentor would have either made them feel more confident (16, or 94%) or altered their handling of recent cases (15, or 88%). All residents had access to a mobile phone, and many (11, or 65%) had used it to contact a medical colleague for guidance. A low-cost and technologically simple telemedicine solution that maximized use of mobile phone capability, provided access to medical and health-care information, and permitted exchange of images would be an appropriate response to the identified needs.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".