Toward a Better Understanding of the Assimilation of Telehealth Systems
Bibliographic record
Abstract
A number of healthcare authorities are considering bringing telehealth systems out of experimental settings into mainstream clinical care. As most literature on telehealth systems to date has focused on their adoption and their evaluation, more work is warranted to understand how telehealth systems can be assimilated and to identify factors that may facilitate or impinge onto this assimilation. Borrowing from institutional, structuration and organizational learning theories, we propose a conceptual model of the determinants relevant for the assimilation of telehealth systems in healthcare organizations. The result is summarized in eight conjectures and a conceptual model. This work not only goes beyond the common methods of analyzing and discussing telehealth systems with user acceptance models, but it also draws a strong link between the assimilation of technological innovations and their institutional context. We hope it will contribute to guide research and managerial actions directed toward integrating telehealth systems in the workplace.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".