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Record W2806013538 · doi:10.1186/s12913-018-3103-0

A study of leading indicators for occupational health and safety management systems in healthcare

2018· article· en· W2806013538 on OpenAlexaffabout
Joan Almost, Elizabeth G. VanDenKerkhof, Peter Strahlendorf, Louise Caicco Tett, Joanna Noonan, Thomas J. Hayes, Henrietta Van hulle, Jeremy Holden, Tracy Kent-Hillis, Geneviève C. Paré, Karanjit Lachhar, Vanessa Silva e Silva

Bibliographic record

VenueBMC Health Services Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsHotel Dieu HospitalKingston Health Sciences CentreOttawa HospitalKingston General HospitalGroup Health CentreToronto Metropolitan UniversityQueen's University
Fundersnot available
KeywordsHealth careHealth administrationOccupational safety and healthMedicinePatient safetyLaggingHealth informaticsEnvironmental healthFocus groupOvertimeNursing researchPublic healthNursingBusinessMarketingEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: In Ontario, Canada, approximately $2.5 billion is spent yearly on occupational injuries in the healthcare sector. The healthcare sector has been ranked second highest for lost-time injury rates among 16 Ontario sectors since 2009 with female healthcare workers ranked the highest among all occupations for lost-time claims. There is a great deal of focus in Ontario's occupational health and safety system on compliance and fines, however despite this increased focus, the injury statistics are not significantly improving. One of the keys to changing this trend is the development of a culture of healthy and safe workplaces including the effective utilization of leading indicators within Occupational Health and Safety Management Systems (OHSMSs). In contrast to lagging indicators, which focus on outcomes retrospectively, a leading indicator is associated with proactive activities and consists of selected OHSMSs program elements. Using leading indicators to measure health and safety has been common practice in high-risk industries; however, this shift has not occurred in healthcare. The aim of this project is to conduct a longitudinal study implementing six elements of the Ontario Safety Association for Community and Healthcare (OSACH) system identified as leading indicators and evaluating the effectiveness of this intervention on improving selected health and safety workplace indicators. METHODS: A quasi-experimental longitudinal research design will be used within two Ontario acute care hospitals. The first phase of the study will focus on assessing current OHSMSs using the leading indicators, determining potential facilitators and barriers to changing current OHSMSs, and identifying the leading indicators that could be added or changed to the existing OHSMS in place. Phase I will conclude with the development of an intervention designed to support optimizing current OHSMSs in participating hospitals based on identified gaps. Phase II will pilot test and evaluate the tailored intervention. DISCUSSION: By implementing specific elements to test leading indicators, this project will examine a novel approach to strengthening the occupational health and safety system. Results will guide healthcare organizations in setting priorities for their OHSMSs and thereby improve health and safety outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.205
GPT teacher head0.580
Teacher spread0.375 · 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 teacher head, not a consensus.

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

Citations49
Published2018
Admission routes2
Has abstractyes

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