Gaps to Best Practices for Teleconsultations Performed by General Practitioners: A Descriptive Cross-Sectional Study
Bibliographic record
Abstract
The COVID-19 pandemic led to large increases in telemedicine activity worldwide. This rapid growth, however, may have impacted the quality of care where compliance with guidelines and best practices are concerned. The aim of this study was to describe the recent practices of a telemedicine activity (teleconsultations) and the breaches of best practice guidelines committed by general practitioners (GPs) in the Greater Eastern Region of France. A cross-sectional study was conducted using a 33-item questionnaire and was provided to the Regional Association of Healthcare Professionals, Union Régionale des Professionnels de Santé (URPS) to be shared amongst the GPs. Between April and June 2021, a total of 233 responses were received, showing that (i) by practicing telemedicine in an urban area, (ii) performing a teleconsultation at the patient’s initiative, and (iii) carrying out more than five teleconsultations per week were factors associated with a significantly higher level of best practices in telemedicine. All in all, roughly a quarter of GPs (25.3%, n = 59) had a self-declared good telemedicine practice, and the rules of good practice are of heterogeneous application. Despite the benefits of learning on the job for teleconsultation implementation during the COVID-19 lockdowns, there may be a clear need to develop structured and adapted telemedicine training programs for private practice GPs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".