Impact of Training Modules on Physicians’ Perspective of COVID-19: An Online Survey
Bibliographic record
Abstract
Background: The outbreak of COVID-19 has remained a massive challenge for healthcare workers specially physicians. Effective professional training has a crucial role in preparing doctors for responding to pandemics. Objective: To assess the effectiveness of existing training modules on enhancing knowledge, ensuring safe practice, and improving behavior on COVID-19 among physicians. Methods: This is a descriptive, cross-sectional, online survey; where a virtual questionnaire was used to collect data through online professional platforms. A pre-tested survey tool was employed to assess the impact of professional training on infection prevention and control. Results: Total 161 physicians participated in this survey from 15 different countries. Most of the respondents (72%) received training from various sources like the workplace (60%) and international agencies (21%), through the in-person or online format. Knowledge assessment revealed advanced (43%) and competent (40%) understanding by the participants. Improving knowledge progression was displayed by the cohort who received professional training (p<0.00). Physicians’ positive behavior and good practices were observed with the training modules. Conclusion: It became evident from this study, that professional training is effective in enhancing knowledge, improving behavior, and ensuring safe practices. Hence, designing such training modules for the physicians is warranted to tackle ongoing and future pandemics. J MEDICINE 2021; 22: 107-113
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".