UWorkItOut UWin Program: A RE-AIM Framework Program Evaluation
Bibliographic record
Abstract
The UWorkItOut UWin program is a 6-week exercise training and counseling intervention aimed to improve the mental health of university students. Benefits of the program include a reduction of anxiety and depression symptoms, physical, psychological, and cognitive outcomes, as well as the development of various coping skills. Despite these favorable outcomes, a process evaluation of the program is needed to understand its effectiveness. The present study conducted a process evaluation using the RE-AIM framework to assess the UWorkItOut UWin program. Data collected from a mixed-method quasi-experimental pretest-posttest single group design was analyzed using the RE-AIM dimensions (i.e. Reach, Effectiveness, Adoption, Implementation, and Maintenance). Sixty participants (n = 60) completed pre-post surveys, exercise training data, self-report physical activity questionnaires, and exit interviews. The program performed well across the RE-AIM dimensions. The program had high adherence rates (reach) and positive outcomes related to mental health, such as decreased anxiety (d = −0.58), depression (d = −0.36), and loss of emotional control (d = −0.42) as well as increased general positive affect (d = 0.47) emotional ties (d = 0.40), and increased physical activity levels (effectiveness). Program staff were valuable for the social support they provided to participants (adoption). However, the program faced barriers with respect to securing dedicated space to conduct the program (implementation) and lacked evidence of long-term maintenance of physical activity participation (maintenance). The program prioritizes a client-centered approach to meet the needs of the participants. As such, continued program evaluation and adaptation is needed.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".