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Record W2094016805 · doi:10.1186/1748-5908-7-77

Using psychological theory to inform methods to optimize the implementation of a hand hygiene intervention

2012· article· en· W2094016805 on OpenAlexafffund
Véronique Boscart, Geoff Fernie, Jae H Lee, Susan Jaglal

Bibliographic record

VenueImplementation Science · 2012
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoConestoga College
FundersCanadian Institutes of Health ResearchToronto Rehabilitation InstituteOntario Ministry of Health and Long-Term Care
KeywordsNursingMedicineIncentiveQualitative researchIntervention (counseling)Health administrationHealth informaticsHygieneNursing researchMedical educationPublic health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0020.006
Scholarly communication0.0060.004
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.280
GPT teacher head0.650
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations96
Published2012
Admission routes2
Has abstractyes

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