The paradoxes of telehealth platforms: what did we learn from the use of telehealth platforms?
Bibliographic record
Abstract
This article is an overview and reflection of the findings of an evaluative study conducted on a program called "Techno-Covid Partnership" (TCP) implemented in April 2020 at the Centre Hospitalier de l'Université de Montréal (CHUM) in Montreal, Canada. In the context of the COVID-19 pandemic, the CHUM decided in April 2020 to implement telehealth, virtual care, and telemonitoring platforms and technologies to maintain access to care and reduce the risks of contamination and spread of COVID-19 as well as to protect users of health services and health professionals. Three technological platforms for telehealth and remote care and monitoring have been developed, implemented, and evaluated in real-time within the framework of the TCP program. A cross-sectional study was carried out in which a questionnaire was used and administered to users of telehealth platforms including patients and healthcare professionals. The methods and results of the study have been published previously published. In the completion of the two articles published in this context, in this paper, we briefly recall the context of the study and the method performed. The main focus of the paper is on presenting a critical overview and reflection on the major findings of our evaluation of the use of telehealth platforms from the point of view of patients and health professionals and discuss certain paradoxes i.e., the advantages, challenges, recommendations, and other perspectives that emerged in this study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".