An Assessment of the Telehealth Needs and Health-Care Priorities of Tanna Island: A Remote, Under-Served and Vulnerable Population
Bibliographic record
Abstract
We surveyed eight Canadian physicians who had each provided medical care for six months on the remote and under-served island of Tanna in Vanuatu. The most frequently encountered medical problems on Tanna were infectious diseases (tuberculosis, hepatitis, abscesses, malaria, pneumonia, typhoid fever, meningitis and skin infections). When physicians were asked about the top three health-care priorities, they ranked tuberculosis control, clean water and improved health-care delivery/communication between hospital and outposts as most important. The key issues were: (1) basic public health needs and infrastructure development are higher in priority than telehealth; (2) telehealth consultants must have knowledge pertinent to local conditions and resources available to the population; (3) electronic equipment suited to tropical environments is needed; (4) projects must be developed locally rather than internationally. Understanding how telehealth can provide support to health professionals under challenging conditions may assist with the health priorities in developing countries and potentially provide access to resources both locally and internationally.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".