Protocol of a randomized controlled trial of sun protection interventions for operating engineers
Bibliographic record
Abstract
BACKGROUND: Skin cancer are increasing and some types of skin cancer are among the most lethal cancers yet are easily preventable. However, sun protection interventions are rarely implemented among outdoor workers. Our prior work shows that Michigan Operating Engineers (heavy equipment operators) spend an average of 4-5 hours in the sun, about one-third reported getting sun burned at least once a summer, and over half burned more than once a summer. About three-quarters of the sample never or only sometimes used sun block. METHODS/DESIGN: Using the Health Belief Model as a guide, this randomized controlled trial (RCT) will test the efficacy of four sun protection interventions targeting Operating Engineers: a) education only; b) education and mailed sunscreen; c) education and text message reminders; and, d) education, mailed sunscreen, and text message reminders. Participations in the study will be offered during regularly scheduled safety trainings at the Local 324 Training Center. Pre- and post-intervention surveys will be collected to determine changes in sunscreen use and sun burning, the primary dependent variables. The analyses will include: a) paired t-tests to determine changes over time (from pre-intervention to post-intervention) in outcome variables (sunscreen use and burning) separately in the 4 intervention groups, b) Repeated Measures Analysis of Variance (RM-ANOVA) to compare the changes in outcomes across the 4 groups, and c) t-tests on change scores as follow-ups to the RM-ANOVA to determine exactly which groups differ from each other. DISCUSSION: Based on the outcome of this study, we will develop a RO1 for wider scale testing and dissemination in conjunction with the International Training Center which services North America (including the US, Mexico, and Canada). Wide scale dissemination of an efficacious sun protection intervention has the potential to substantially impact skin cancer rates among this population. The ultimate goal is for high reach, high efficacy, and low cost. TRIAL REGISTRATION: NCT01804595.
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 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 | 0.046 | 0.043 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.095 | 0.015 |
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".