The relevance of telehealth across the digital divide: The transfer of knowledge over distance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the concept of relevance as an explanatory factor to the diffusion of IT-use, or, in this paper particularly, the use of Telehealth. Relevance is the net value of performance expectancy and effort expectancy and contains both micro-relevance (i.e. here-and-now) and macro-relevance (i.e. actual goals). Following the case-study approach, two Telehealth situations were studied in Rwanda and The Netherlands. In the comparison, two more existing studies in Canada and Tanzania were included. The conclusion is that relevance is the explanatory factor, whereas particularly micro-relevance is crucial. Without the micro-relevant occasions that initiate use, there will be no use on longer term. In the cases studied the micro-relevance of knowledge-transfer was crucial. Furthermore distance determined Telehealth relevance. Practical conclusions to cases were drawn.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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