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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
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