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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".