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Injury Rates, Predictors of Workplace Injuries, and Results of an Intervention Program Among Community Health Workers

2007· article· en· W2139090004 on OpenAlex
Kevin J.P. Craib, Georgina Hackett, Chris Back, Yuri Cvitkovich, Annalee Yassi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePublic Health Nursing · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersCanadian Institutes of Health ResearchWorkSafeBC
KeywordsPsychological interventionMedicineOccupational safety and healthInjury preventionPopulationPoison controlIncidence (geometry)Intervention (counseling)Suicide preventionCommunity healthEnvironmental healthPhysical therapyPublic healthNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Few incidence studies of workplace injuries among community health workers exist, and evidence regarding the effectiveness of interventions in this population is lacking. OBJECTIVES: To determine the incidence of workplace injury among community health workers in British Columbia; to identify predictors of injury; and to assess the effectiveness of a multicomponent intervention program in this population. METHODS: Data were collected from an intervention study of 648 community health workers from six agencies to calculate injury rates. Interventions included an education and training module, a risk assessment tool and resource guide, and a lift equipment registry. RESULTS: The majority of injuries were attributed to overexertion and falls. Annual incidence rates were 20.7% for reported injuries, and 8.1% for time-loss injuries. A history of previous injuries and working full time were predictors of time to first injury report. Participants who received an intervention were significantly more likely to report workplace injuries than participants in the comparison group, but were less likely to incur a time-loss injury. CONCLUSIONS: The interventions used in this study led to increased awareness and an increase in reported injuries but resulted in fewer time-loss injuries. The mechanisms that led to these findings need to be explored further.

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.

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)0.033
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.102
GPT teacher head0.518
Teacher spread0.416 · 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