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Record W2567546987

The Hawthorne Effect in Hand Hygiene Compliance Monitoring

2014· dissertation· en· W2567546987 on OpenAlexfundno aff
Jocelyn A. Srigley

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicManagement Theory and Practice
Canadian institutionsnot available
FundersAstellas PharmaAssociation of Medical Microbiology and Infectious Disease Canada
KeywordsHawthorne effectHygieneCompliance (psychology)MedicinePsychologyComputer securityComputer scienceSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Introduction: The Hawthorne effect, or behaviour change due to awareness of being observed, is believed to inflate directly observed hand hygiene compliance rates, but evidence is limited. \nMethods: A real-time location system tracked hospital hand hygiene auditors and recorded alcohol-based hand rub and soap dispenses. Rates of hand hygiene events per dispenser per hour within sight of auditors were compared to dispensers not exposed to auditors. \nResults: The event rate in dispensers visible to auditors (3.75/dispenser/hour) was significantly higher than unexposed dispensers at the same time (1.48) and in prior weeks (1.07). The rate increased significantly when auditors were present compared to five minutes prior to arrival. There were no significant changes inside patient rooms. \nConclusions: Hand hygiene event rates increase in hallways when auditors are visible and the increase occurs after the auditors’ arrival, consistent with the existence of a Hawthorne effect localized to areas where auditors are visible.

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)0.018
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.068
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.018
GPT teacher head0.256
Teacher spread0.238 · 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.

Study designObservational
DomainMethods
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

Citations2
Published2014
Admission routes1
Has abstractyes

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