Systematic review: Factors related to injuries in small- and medium-sized enterprises
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".