Bibliographic record
Abstract
During the past few decades, many healthcare authorities sought to integrate new methods of delivering care to patients. Among the priorities faced by these organizations, a major issue arose of how to provide healthcare to patients who live in rural or remote regions suffering from a lack of accessible professional resources and services that comply with WHO’s call for providing fair access to healthcare. Many attempts were made to integrate new technologies such as telehealth into the healthcare system, but in many cases, telehealth was not successful due in part to limited assimilation into healthcare organizations and work practices. Telehealth addresses operational issues such as a shortage of professionals in rural or underserved geographical regions. Using a breadth of reference theories such as institutional theory, structuration theory, and organizational learning theory, we propose a conceptual model that integrates the determinants of telehealth assimilation and identifies factors that impinge upon the process of assimilation. We posit that telehealth assimilation can only be understood by taking a multilevel approach to the phenomenon, whereby assimilation starts at the individual level, permeates through other organizational levels such as groups, and finally ends at the organizational and inter-organizational level. Further, assimilation of technological innovations must be considered within their institutional context. Derived from our conceptual model, we make several propositions and hope that our work will significantly guide future research and managerial actions geared toward integrating healthcare in the workplace.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".