Impact of an online course on infection control and prevention competencies
Bibliographic record
Abstract
AIM: This paper is a report of a study to examine the impact of an online course on nurses' and allied healthcare professionals' competency in infection prevention and control and the influence of organizational climate on knowledge transfer. BACKGROUND: Nosocomial infection, globalization, international travel and bacterial resistance are among the factors contributing to heightened awareness of the importance of infection prevention and control in today's healthcare environment. An online course in infection control was developed to facilitate the delivery of standardized training to large numbers of health providers. METHOD: A quasi-experimental, pre-and post-test study using questionnaires and open-ended questions was conducted in 2006 with a convenience sample of 76 healthcare professionals, the majority of whom were Registered Nurses. FINDINGS: Participants made statistically significant increases in their perceptions of competency in infection control following the course. The majority were very satisfied with the course and reported that what they had learned was useful and relevant to their practice. Participants who worked in supportive organizations that were open to change reported a higher incidence of knowledge transfer activities. Two course design features in particular, video and interactive quizzes and games, motivated learners and enhanced the learning experience. CONCLUSION: Online learning can provide ongoing, convenient and effective access to up-to-date information on best practices in infection control and prevention. This standardized delivery approach minimizes demand on limited training resources which are under strain and gives learners the opportunity to refresh 'rusty' infection control and prevention skills.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".