Partial Examination of the Public Health Impact of the People with Arthritis Can Exercise (PACE<sup>®</sup>) Program: Reach, Adoption, and Maintenance
Bibliographic record
Abstract
OBJECTIVE: To partially evaluate the public health impact (i.e., reach, adoption, maintenance) of People with Arthritis Can Exercise (PACE) programs, which were initiated as a result of two PACE instructor-training workshops. DESIGN: The study design involved a one-time only, cross-sectional assessment of reach, adoption, and maintenance, conducted 6 months after the workshops. SAMPLE: Participants were 11 adults (n(females)=10) trained to be PACE instructors at one of the workshops. MEASUREMENTS: One-on-one phone interviews, developed using the RE-AIM framework, assessed reach, adoption, and maintenance. RESULTS: Eight of the 11 individuals trained as instructors subsequently began PACE in one of 10 organizations across various communities, indicating high program adoption. However, on average, only 7 individuals with arthritis participated in each PACE program, indicating a low program reach. Within 6 months of beginning PACE, only 3 organizations continued to offer PACE, indicating low program maintenance. Two primary challenges to initiating PACE included recruiting a sufficient number of people to participate in the program and in finding a convenient time to offer it so more individuals could join. CONCLUSION: The public health impact, as assessed by reach, adoption, and maintenance, of PACE programs initiated as a result of 2 instructor-training workshops was low.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".