UWALK: the development of a multi-strategy, community-wide physical activity program
Bibliographic record
Abstract
UWALK is a multi-strategy, multi-sector, theory-informed, community-wide approach using e and mHealth to promote physical activity in Alberta, Canada. The aim of UWALK is to promote physical activity, primarily via the accumulation of steps and flights of stairs, through a single over-arching brand. This paper describes the development of the UWALK program. A social ecological model and the social cognitive theory guided the development of key strategies, including the marketing and communication activities, establishing partnerships with key stakeholders, and e and mHealth programs. The program promotes the use of physical activity monitoring devices to self-monitor physical activity. This includes pedometers, electronic devices, and smartphone applications. In addition to entering physical activity data manually, the e and mHealth program provides the function for objective data to be automatically uploaded from select electronic devices (Fitbit®, Garmin and the smartphone application Moves) The RE-AIM framework is used to guide the evaluation of UWALK. Funding for the program commenced in February 2013. The UWALK brand was introduced on April 12, 2013 with the official launch, including the UWALK website on September 20, 2013. This paper describes the development and evaluation framework of a physical activity promotion program. This program has the potential for population level dissemination and uptake of an ecologically valid physical activity promotion program that is evidence-based and theoretically framed.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".