Clinician satisfaction and experience using teleconsultation during the COVID-19 pandemic in Pakistan: A cross-sectional study
Bibliographic record
Abstract
Aims: During the pandemic of COVID-19, the sudden change in traditional health-care providing systems, clinicians experience some positive and negative aspects of the approach. This study evaluates the clinician's satisfaction and experience with the use of teleconsultation provided during the pandemic of novel coronavirus and their willingness to continue telehealth after the pandemic. Materials and Methods: A cross-sectional survey was conducted online during the peak pandemic of COVID-19 in Pakistan through Google Forms questionnaire from 115 health consultants on different disciplines and recruited through social media. The questionnaire contains 15 questions regarding clinician's satisfaction, quality of treatment, and intention to continue providing telehealth services after the pandemic. Descriptive and inferential statistics were obtained by analyzing the data using SPSS software version 20, USA. Results: One hundred and fifteen consultants, 28 males and 87 females participated in the study, in which 62% were found to have an average and 34% at a high level of satisfaction. The Kruskal–Wallis test showed a significant difference among different medical specialists in the continuation of telehealth services after the pandemic of COVID-19 (P = 0.003) and its recommendation to friends and family (P = 0.02) with high mean rank in endocrinologist and dermatologist. Conclusions: A great number of participants reported a good response for the continuation in telemedicine services in their daily routine even after the pandemic situation. However, there is an urgent need to find the solution for the difficulties and drawbacks faced by health-care providers.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".