Virtual Vs In-Personal Teaching of Infection Control Essentials: A Quasi-Experimental Multi-Center Nonequivalent Groups Design
Bibliographic record
Abstract
BACKGROUND In setting of the current COVID-19 pandemic, it is crucial to endorse infection control competencies. However, whether virtual training is equivalent to in-person teaching to develop such competencies requires further elucidation. AIM We aim to explore the effect of a brief, three-to-five-minute training session on infection control competencies in the major area of emergency department infection control, using virtual versus in-person training. METHODS Two hospitals were chosen, and the study design was a quasi-experimental multi-centre nonequivalent groups design. RESULT The learning score increased from 39.06%, SD=17.18 (95% CI 32.39-45.72) to 52.48%, SD=26.48 (95% CI 44.01-60.95) in the virtual training group, and from 47.86%, SD=22.51 (95% CI 41.47-54.26) to 79.65%, SD=21.45 (95% CI 70.14-89.16) after the in-person teaching. The mean difference between the two groups revealed a higher learning score using in-person teaching: 27.16%; t(60)=-4.12, p = 0.0001. CONCLUSION Infection control competencies are better acquired via in-person teaching than by virtual education.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".