When Passion and Compassion Lead to a Technological Innovation: Telehealth Systems
Bibliographic record
Abstract
With the emergence of COVID-19, Telehealth became one of the subjects most discussed around the world as a solution to provide health service. Nowadays more than ever, we are facing a limitation of resources available, especially human and technological. As a consequence of these limitations, having in person appointment became almost impossible. Our aim in this article is to propose a better understand of the Telehealth and how it can be a good solution in this new context which we are living. We start discussing the specificities of the health environment and how the management literature applied to achieve success consider the passion and we make a review on the theories linked with the subject; then we propose some conjecture which can help the assimilation and implementation of a new system to provide. This paper concludes with a proposal of a multilevel modeling approach that enables stakeholders to gain a better understanding of the assimilation of information systems, based on their nature and the issues associated with their development.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".