HealtheSteps Process Evaluation
Bibliographic record
Abstract
HealtheSteps is a 6-month lifestyle program, whereby participants at risk for chronic disease meet bi-monthly with a trained HealtheSteps coach to set prescriptions in the areas of physical activity, exercise, and healthy eating. PURPOSE: A process evaluation was conducted alongside a pragmatic randomized controlled trial to explore the acceptability of delivering HealtheSteps to participants at risk for chronic disease by members of the community working at primary care and health services organizations in Southwestern Ontario. METHODS: Data for the process evaluation included interviews with trained HealtheSteps coaches post-program (month 6) and interviews with participants, 6 months post- program (month 12). All coach interviews (n=12) and a purposeful sample of participant interviews (n=13) were analyzed separately. The sample of participant interviews were selected based on maximum variation in terms of site location, age, gender, ethnicity, marital status, education, occupation, body mass index, average daily step count, and self- rated health. Transcripts were read through by the research team; key themes and exemplar quotes to support these themes were then identified and summarized. RESULTS: Coaches found HealtheSteps was easy to deliver as the focus was only on three key risk factors for chronic disease. Coaches noted group sessions, ensuring participants had the same coach at every session, and evaluating participant readiness prior to beginning the program, could improve the program for future delivery. Participants spoke positively of their coaches and found the program promoted accountability over their healthy lifestyle changes through tracking progress and step counts on the pedometer, and meeting with their coach. Participant suggestions to improve the program included providing pedometers for participants to continue to monitor physical activity, and providing opportunities for the participants to be accountable to their lifestyle changes long-term, once the formal in-person coaching sessions are complete. CONCLUSIONS: HealtheSteps is an acceptable program for improving the lifestyle habits of individuals at risk for chronic disease. Moving forward, the suggestions for improving the program delivery do not require significant changes to the program protocol.
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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 | 0.196 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.006 |
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".