A Readiness Model for Telehealth Is it possible to Pre-Determine How Prepared Communities are to Implement Telehealth?
Bibliographic record
Abstract
Telehealth "readiness" can be defined as the degree to which users, health care organizations, and the health system itself are prepared to participate and succeed in its application. This project developed a readiness model for rural/remote locations in Canada. Specifically defined groups or communities with shared characteristics within a rural geographical community (i.e. practitioners, patients, the public, and health care organizations) participated in key informant interviews, awareness sessions, focus groups, and face-to-face interviews. The data were examined and organized keeping in mind Weiss' Program's Theory of Change. This approach allowed concrete and abstract factors to be considered. The model that emerged suggests that there are four types of readiness for each of the defined communities: core, engagement, structural, and non-readiness. The "communities" share some readiness factors and risks, but also exhibit unique elements. This finding is critical to acknowledge when the goal is to implement a useful, effective, and sustainable telehealth system within remote settings. Study results hold a key to understanding why technology systems have failed in the past, in spite of dedicating considerable human and financial resources towards their implementation. Notations of these findings will be helpful in future telehealth implementations within rural and isolated areas.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".