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Record W2520639393 · doi:10.5430/jha.v5n6p46

Management commitment to safety, teamwork, and hospital worker injuries

2016· article· en· W2520639393 on OpenAlexvenueno aff
Alyssa K. McGonagle, Lynnette Essenmacher, Lydia Hamblin, Mark Luborsky, Mark J. Upfal, Judith E. Arnetz

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
FundersNational Institute for Occupational Safety and HealthNational Institute of Mental HealthNational Institute on AgingCenters for Disease Control and Prevention
KeywordsTeamworkPatient safetyNursingOrganizational cultureWork (physics)MedicineHealth carePublic relationsManagementEngineeringPolitical science

Abstract

fetched live from OpenAlex

safety is lacking. This study addresses this gap by providing evidence linking teamwork perceptions in hospital workers to worker injuries, and further, finds a linkage between manager commitment to safety and teamwork. Organizational records of worker injuries and survey responses regarding management commitment to safety and teamwork from 446 hospital workers within 42 work units in a multi-site hospital system were examined. Results underscored the particular importance of teamwork on worker injuries as well as the importance of management commitment to safety as relating to teamwork. To improve worker safety, organizational leaders and unit managers should work to maintain environments wherein teamwork can thrive.

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)0.004
metaresearch head score (Gemma)0.029
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.406
Teacher spread0.381 · 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

Citations44
Published2016
Admission routes1
Has abstractyes

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