Barriers to infection control routine practices and problem-solving strategies among nursing students and instructors – A cross-sectional survey
Bibliographic record
Abstract
Background: High nursing workload, negative role models, and inconvenient location of alcohol-based hand-rub dispensers are among the most common barriers that prevent nursing students and nurses from adhering to Routine Practices (RP). The aim of this study was to identify if nursing students and instructors encountered these three barriers, what strategies they used to address them, and how confident they were about applying problem solving in addressing them. Method: A cross-sectional survey was conducted in three nursing schools in Eastern Canada, and included 577 undergraduate nursing students, and 20 nursing instructors. Data were collected using the Routine Practices Problem-Solving Questionnaire. Frequency distributions were used to describe the participants’ characteristics and each item in the questionnaire. Pearson chi-square test was used to assess relationships between the level of confidence and participants’ characteristics. Results: We found that only 25% to 44.2% of students and instructors reported that high nursing workload, negative role models, and inconvenient location of alcohol-based hand rub were among the most common barriers which prevented them from adhering to Routine Practices. Although they encountered these three barriers, only 21.1% to 30.2% of students indicated that they used problem solving to address them. However, both groups of participants identified other strategies that can be used to address these three barriers. More instructors, compared to students, were very confident/confident about applying problem solving to address these barriers. There was a significant association between nursing students’ levels of confidence and their training in RP as well as their training in problem solving related to RP (p< .0001), but no significant association between the instructors’ level of confidence and these characteristics (p > 0.05). Conclusion: Understanding of these three barriers, and knowing possible strategies to address them, including problem solving, can potentially help infection control practitioners in their discussions with nurses, students, and instructors to improve adherence to RP.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".