The rise of telemedicine in primary care: understanding patients’ and healthcare workers’ perspectives on acceptability of the COVID-19 remote care model
Bibliographic record
Abstract
literature search, G -Funds CollectionBackground.Remote monitoring of vital signs combined with teleconsultations has been proposed as an effective method of care for COVID-19 patients.However, the acceptability of the remote care model has yet to be explored.Objectives. to explore patients' and healthcare workers' views and experiences with the COVID-19 remote care programme using the theoretical Framework of acceptability (tFa) and identify challenges for further dissemination.Material and methods.a qualitative study was conducted with 25 patients and 5 healthcare workers from three primary care centres in Slovenia who participated in the COVID-19 remote care programme.In-depth interviews, informed by the tFa, were conducted by phone or face-to-face, recorded and transcribed verbatim.transcripts were analysed by two independent researchers using a template content analysis guided by seven tFa domains, with the possibility of inductively adding codes from the text as needed.Results. the remote care model proved acceptable to patients and healthcare workers in all seven tFa domains.For patients, the remote care model provided a sense of security, physician interest and the ability to detect deterioration in health early, while healthcare workers found it useful for triaging and protecting against disease transmission.However, both reported the additional burden and risk of unreliable pulse oximetry readings.Conclusions.the remote care model proved acceptable and scalable to other respiratory diseases in primary care.key challenges to further scaling include the complexity of system design and data sharing, the uncertain role of registered nurses and family members and the need to implement follow-up programmes focusing on self-management behaviours.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".