Using psychological theory to inform methods to optimize the implementation of a hand hygiene intervention
Bibliographic record
Abstract
BACKGROUND: Careful hand hygiene (HH) is the single most important factor in preventing the transmission of infections to patients, but compliance is difficult to achieve and maintain. A lack of understanding of the processes involved in changing staff behaviour may contribute to the failure to achieve success. The purpose of this study was to identify nurses' and administrators' perceived barriers and facilitators to current HH practices and the implementation of a new electronic monitoring technology for HH. METHODS: Ten key informant interviews (three administrators and seven nurses) were conducted to explore barriers and facilitators related to HH and the impact of the new technology on outcomes. The semi structured interviews were based on the Theoretical Domains Framework by Michie et al. and conducted prior to intervention implementation. Data were explored using an inductive qualitative analysis approach. Data between administrators and nurses were compared. RESULTS: In 9 of the 12 domains, nurses and administrators differed in their responses. Administrators believed that nurses have insufficient knowledge and skills to perform HH, whereas the nurses were confident they had the required knowledge and skills. Nurses focused on immediate consequences, whereas administrators highlighted long-term outcomes of the system. Nurses concentrated foremost on their personal safety and their families' safety as a source of motivation to perform HH, whereas administrators identified professional commitment, incentives, and goal setting. Administrators stated that the staff do not have the decision processes in place to judge whether HH is necessary or not. They also highlighted the positive aspects of teams as a social influence, whereas nurses were not interested in group conformity or being compared to others. Nurses described the importance of individual feedback and self-monitoring in order to increase their performance, whereas administrators reported different views. CONCLUSIONS: This study highlights the benefits of using a structured approach based on psychological theory to inform an implementation plan for a behavior change intervention. This work is an essential step towards systematically identifying factors affecting nurses' behaviour associated with HH.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".