Understanding Small Business Engagement in Workplace Violence Prevention Programs
Bibliographic record
Abstract
PURPOSE: Worksite wellness, safety, and violence prevention programs have low penetration among small, independent businesses. This study examined barriers and strategies influencing small business participation in workplace violence prevention programs (WVPPs). APPROACH: A semistructured interview guide was used in 32 telephone interviews. SETTING: The study took place at the University of North Carolina Injury Prevention Research Center. PARTICIPANTS: Participating were a purposive sample of 32 representatives of small business-serving organizations (e.g., business membership organizations, regulatory agencies, and economic development organizations) selected for their experience with small businesses. INTERVENTION: This study was designed to inform improved dissemination of Crime Free Business (CFB), a WVPP for small, independent retail businesses. METHODS: Thematic qualitative data analysis was used to identify key barriers and strategies for promoting programs and services to small businesses. RESULTS: Three key factors that influence small business engagement emerged from the analysis: (1) small businesses' limited time and resources, (2) low salience of workplace violence, (3) influence of informal networks and source credibility. Identified strategies include designing low-cost and convenient programs, crafting effective messages, partnering with influential organizations and individuals, and conducting outreach through informal networks. CONCLUSION: Workplace violence prevention and public health practitioners may increase small business participation in programs by reducing time and resource demands, addressing small business concerns, enlisting support from influential individuals and groups, and emphasizing business benefits of participating in the program.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".