The Impact of Organizational Characteristics on Telehealth Adoption by Hospitals
Bibliographic record
Abstract
Human and organizational factors are central to the adoption of various telehealth technologies and influence their diffusion into integrated networks. The aim of this paper is twofold. Firstly, a conceptual framework for assessing organizational factors that condition telehealth adoption by hospitals is proposed. Secondly, the results of an exploratory study conducted among the 32 hospitals involved in the Provincial Extended Telehealth Network of Quebec (Canada) are presented and discussed. Relevant concepts from different theoretical frameworks were combined to propose a comprehensive framework of potential factors affecting telehealth adoption by hospitals. A questionnaire was administered via telephone interviews to the DSP (Director of Medicine) of each of the 32 hospitals. Level of telehealth adoption was assessed by computing the number of transmissions performed since the hospital’s adhesion to the network. Then, contingency analyses were performed to determine which organizational factors were significantly associated with telehealth adoption. Finally, the paper discusses some of the implications pertaining to the organizational dimensions affecting telehealth adoption by hospitals and proposes avenues to facilitate the diffusion of this technology. 1.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".