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Record W2953532511 · doi:10.4103/ijciis.ijciis_78_18

Systematic review: Factors related to injuries in small- and medium-sized enterprises

2019· review· en· W2953532511 on OpenAlexaff
Behdin Nowrouzi‐Kia, Nirusa Nadesar, Jennifer Casole

Bibliographic record

VenueInternational Journal of Critical Illness and Injury Science · 2019
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcMaster UniversityLaurentian UniversityUniversity of Toronto
Fundersnot available
KeywordsAntecedent (behavioral psychology)MedicineCritical appraisalSystematic reviewOccupational safety and healthPsychological interventionApplied psychologyData extractionHuman factors and ergonomicsPoison controlInjury preventionProtocol (science)MEDLINENursingEnvironmental healthPsychologySocial psychologyAlternative medicine

Abstract

fetched live from OpenAlex

The purpose of this systematic review was to identify the antecedent factors of workplace injuries in small- and medium-sized enterprises (SMEs). A customized systematic review protocol included the research question, literature search, quality appraisal, data management and extraction, and evidence synthesis. The evidence was evaluated using the Critical Appraisal Skills Programme checklists and the Cochrane Collaboration "Risk of Bias" assessment tools. A total of 1355 articles were identified before duplicate removal. Ten articles were relevant to the study objective. Of these, two articles examined antecedents related to physical injuries, three examined those related to psychological injuries, and four focused on a combination. Antecedent factors included older workers, unsafe acts, unsafe working conditions, accident type and type of work performed, trips and falls, loss in productivity, social isolation, financial stress, and lack of employer support during the return to the workplace. The findings of this systematic review support the need for increased research to identify antecedent factors associated with injury in SMEs. Research should focus on interventions to mitigate injury rates that associate employees with employers, thus promoting collaboration in augmenting health and safety in SMEs.

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)0.005
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.059
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.082
GPT teacher head0.537
Teacher spread0.455 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2019
Admission routes1
Has abstractyes

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