Promoting Men’s Health With the “Don’t Change Much” e-Program
Bibliographic record
Abstract
Men’s e-health promotion programs can offer end-user anonymity and autonomy that provide avenues for supporting positive health behavior change. The twofold purpose of the current study was to use a benchmark cohort as a reference group to: (1) describe associations between men’s usage levels of the e-health program Don’t Change Much (DCM) and their recent and intended health behavior changes, and (2) report an exploratory analysis of the moderating effects of demographic variables on the associations between DCM users and their recent and intended health behavior changes. Based on self-report, DCM users were classified into limited ( n = 613, 34.7%), low ( n = 826, 46.8%), and high ( n = 327, 18.5%) exposure groups. Compared with the benchmark cohort, DCM high-exposure respondents had significantly increased odds for eight of the nine recent behavior changes, with the largest effect size observed for “Made an effort to sit less and walk more” (odds ratio [OR] 2.996, 95% CI [2.347, 3.826]). Eight of the nine intended health behavior changes in the DCM high-exposure group had significantly increased odds compared to the benchmark cohort, with “Reduce stress level” (OR 3.428, 95% CI [2.643, 4.447]) having the largest effect size. Significantly greater total numbers of recent ( F(12, 2850) = 29.32; p = .001; R 2 = .086) and intended health behavior changes ( F(12, 2850) = 34.59; p = .001; R 2 = 0.100) were observed among high exposure respondents while adjusting for demographics. Younger age, being employed, and household income <$120,000 had an enhancing moderator effect on DCM users’ number of intended behavior changes.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".