Fitness facility staff demonstrate high fidelity when implementing an evidence-based diabetes prevention program
Bibliographic record
Abstract
Translating evidence-based diabetes prevention programs into the community is needed to make promising interventions accessible to individuals at-risk of type 2 diabetes. To increase the likelihood of successful translation, implementation evaluations should be conducted to understand program outcomes and provide feedback for future scale-up sites. The purpose of this research was to examine the delivery of, and engagement with, an evidence-based diet and exercise diabetes prevention program when delivered by fitness facility staff within a community organization. Ten staff from a community organization were trained to deliver the diabetes prevention program. Between August 2019-March 2020, 26 clients enrolled in the program and were assigned to one of the ten staff. Three fidelity components were accessed. First, staff completed session-specific fidelity checklists (n = 156). Second, two audio-recorded counseling sessions from all clients underwent an independent coder fidelity check (n = 49). Third, staff recorded client goals on session-specific fidelity checklists and all goals were independently assessed for (a) staff goal-setting fidelity, (b) client intervention receipt, and (c) client goal enactment by two coders (n = 285). Average self-reported fidelity was 90% for all six sessions. Independent coder scores for both counseling sessions were 83% and 81%. Overall staff helped clients create goals in line with program content and had a goal achievement of 78%. The program was implemented with high fidelity by staff at a community organization and clients engaged with the program. Findings increase confidence that program effects are due to the intervention itself and provide feedback to refine implementation strategies to support future scale-up efforts.
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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 | 0.015 | 0.078 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".