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Record W1976878117 · doi:10.2486/indhealth.44.166

Injury Rate as an Indicator of Business Success

2006· article· en· W1976878117 on OpenAlexaff
Theresa Holizki, L. L. Nelson, R. Christopher McDonald

Bibliographic record

VenueIndustrial Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsWorkers Compensation Board of British Columbia
Fundersnot available
KeywordsPayrollWorkers' compensationOccupational safety and healthBusinessSmall businessCompensation (psychology)Operations managementActuarial scienceMarketingMedicineAccountingEngineeringPsychology

Abstract

fetched live from OpenAlex

Health and safety professionals and organizations have often suggested that promoting and improving health and safety in the workplace will improve business success. We conducted a study of all new small businesses that registered with the Workers' Compensation Board of British Columbia (WCB of BC) in the years 1993, 1995, 1996 and 1997, assessing their injury rate in the first 5 complete years of business. The data set represents 53,913 new businesses and 19,332 claims. Businesses were grouped by the number of years between registering for WCB coverage and termination of coverage. Injury rates were determined for each calendar year for each industry sector as injuries per 100 person-years, based on payroll information provided by the businesses. Across all industries, businesses that failed between 1 and 2 yr of start-up had an average injury rate of 9.71 while businesses that survived more than 5 yr had an average injury rate of only 3.89 in their first year of business (p<0.000001). The WCB of BC demonstrated a statistical correlation between health and safety in the workplace and the survival of a small business.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.158
GPT teacher head0.509
Teacher spread0.352 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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