Enhancing Industry-Based Dissemination of an Occupational Sun Protection Program with Theory-Based Strategies Employing Personal Contact
Bibliographic record
Abstract
PURPOSE: Industry-based strategies for dissemination of an evidence-based occupational sun protection program, Go Sun Smart (GSS), were tested. DESIGN: Two dissemination strategies were compared in a randomized trial in 2004-2007. SETTING: The North American ski industry. SUBJECTS: Ski areas in the United States and Canada (n = 69) and their senior managers (n = 469). INTERVENTION: Employers received GSS through a basic dissemination strategy (BDS) from the industry's professional association that included conference presentations and free starter kits. Half of the areas also received the enhanced dissemination strategy (EDS), in which project staff met face-to-face with managers and made ongoing contacts to support program use. MEASURES: Observation of program materials in use and managers' reports on communication about sun protection. ANALYSIS: The effects of two alternative dissemination strategies were compared on program use using PROC MIXED in SAS, adjusted for covariates using one-tailed p values. RESULTS: Ski areas receiving the EDS used more GSS materials (x¯ = 7.36) than those receiving the BDS (x¯ = 5.17; F = 7.82, p < .01). Managers from more areas receiving the EDS reported communicating about sun protection in employee newsletters/flyers (x¯ = .97, p = .04), in guest e-mail messages (x¯ = .75, p = .02), and on ski area Web sites (x¯ = .38, p = .02) than those receiving the BDS (x¯ = .84, .50, .15, respectively). CONCLUSION: Industry professional associations play an important role in disseminating prevention programs; however, active personal communication may be essential to ensure increased implementation fidelity.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".