Examining an Australian physical activity and nutrition intervention using RE-AIM
Bibliographic record
Abstract
Translating evidence-based interventions into community practice is vital to health promotion. This study used the RE-AIM framework to evaluate the larger dissemination of the ManUp intervention, an intervention which utilized interactive web-based technologies to improve the physical activity and nutrition behaviors of residents in Central Queensland, Australia. Data were collected for each RE-AIM measure (Reach, Effectiveness, Adoption, Implementation, Maintenance) using (i) computer-assisted telephone interview survey (N = 312) with adults (18 years and over) from Central Queensland, (ii) interviews with key stakeholders from local organizations (n = 12) and (iii) examination of project-related statistics and findings. In terms of Reach, 47% of participants were aware of the intervention; Effectiveness, there were no significant differences between physical activity and healthy nutrition levels in those aware and unaware; Adoption, 73 participants registered for the intervention and 25% of organizations adopted some part of the intervention; Implementation, 26% of participants initially logged onto the website, 29 and 17% started the web-based physical activity and nutrition challenges, 33% of organizations implemented the intervention, 42% considered implementation and 25% reported difficulties; Maintenance, an average of 0.57 logins and 1.35 entries per week during the 12 week dissemination and 0.27 logins and 0.63 entries per week during the 9-month follow-up were achieved, 22 and 0% of participants completed the web-based physical activity and nutrition challenges and 33.3% of organizations intended to continue utilizing components of the intervention. While this intervention demonstrated good reach, effectiveness, adoption and implementation warrant further investigation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".