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Record W3137527528 · doi:10.1515/ovs-2020-0104

Systematic review and meta-analysis of veterinary-related occupational exposures to hazards

2021· article· en· W3137527528 on OpenAlexaff
Oluwawemimo Adebowale, Olubunmi G. Fasanmi, Babafela Awosile, Monsurat Afolabi, Folorunso O. Fasina

Bibliographic record

VenueOpen Veterinary Science · 2021
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsHealth PEI
Fundersnot available
KeywordsMeta-analysisPhysical hazardOdds ratioMedicineOccupational safety and healthEnvironmental healthOccupational exposureVeterinary medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Understanding hazards within the veterinary profession is critical for developing strategies to ensure the health and safety of personnel in the work environment. This study was conducted to systematically review and synthesize data on reported risks within veterinary workplaces. A systematic review of published data on occupational hazards and associated risk factors were searched within three database platforms namely PubMed, Ebscohost, and Google scholar. To determine the proportion estimates of hazards and pooled odds ratio, two random-effects meta-analysis were performed. For the biological, chemical and physical hazards, the pooled proportion estimates were 17% (95% CI: 15.0-19.0, p < 0.001), 7.0% (95% CI: 6.0-9.0%, p < 0.001) and 65.0% (95% CI: 39.0-91.0%, p < 0.001) respectively. A pooled odds ratio indicated the risk of exposures to physical (OR=1.012, 95% CI: 1.008-1.017, p < 0.001) and biological hazards (OR=2.07, 95% CI: 1.70-2.52, p <0.001) increased when working or in contact with animals. The review has provided a better understanding of occupational health and safety status of veterinarians and gaps within the developing countries. This evidence calls for policy formulation and implementation to reduce the risks of exposures to all forms of occupational-related hazards in veterinary workplaces.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.532
GPT teacher head0.580
Teacher spread0.049 · 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 designMeta-analysis
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

Citations8
Published2021
Admission routes1
Has abstractyes

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