Healthcare workers’ attitudes toward hand hygiene practices: Results of a multicentre qualitative study in Quebec
Bibliographic record
Abstract
Background: Hand hygiene (HH) is one of the most effective practices to reduce healthcare-associated infection (HAI) transmission, though compliance remains inadequate among hospital personnel. The aim of this study was to explore perceived barriers and enablers of HH compliance in hospital care and healthcare workers’ (HCW) HAI risk and severity perceptions. Methods: Qualitative study using semi-structured interviews and observations. Interview recordings were transcribed verbatim and supplemented with transcribed observations and field notes. Data was aggregated and coded thematically using a qualitative data analysis software. Results: 65 interviews and 18 observations with HCWs were conducted in nine hospital centres in Quebec, Canada. Data analysis revealed several factors that may influence HCWs’ compliance with HH recommendations. These included clinical environment factors (e.g., lack of sinks), organizational factors (e.g., inadequate staffing, demanding workloads), and communication factors (e.g., dissemination of infection prevention and control [IPAC] information, feedback, and interpersonal professional relationships). At the individual level, knowledge of IPAC and HAI risk perceptions were associated with the adoption of HH. Conclusion: Understanding the determinants of HH adoption is crucial for improving current practices and reducing HAI rates in hospital care. Our findings suggest that environmental strategies (e.g., additional sinks and HH stations) and organizational and communication strategies (e.g., continuing education and training sessions, support from hospital management, positive feedback) could help raise HCWs’ awareness of HAI prevention and adoption of HH guidelines.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".