By the book: Examining diabetes prevention program coaches’ session content delivery fidelity
Bibliographic record
Abstract
There is extant research examining the effectiveness of health behaviour change programs on clinical outcomes. Examining the fidelity of such programs is equally as critical to ensure these programs are delivered as intended. To increase the probability of program effectiveness, programs must be delivered with high levels of fidelity. One way to examine delivery fidelity is using the Kirkpatrick framework. The third level of the Kirkpatrick framework assesses the extent to which coaches enact a behaviour (i.e., deliver program content) as intended. Small Steps for Big Changes (SSBC) is a diabetes prevention program delivered over six one-on-one sessions to individuals at risk of developing type 2 diabetes. SSBC coaches are trained to deliver exercise- and diet-related information to clients during sessions. The objective of this study was to examine the level of fidelity that SSBC coaches delivered program content. Methods: Nine fitness facility staff were trained to deliver SSBC to clients. For each session, coaches completed checklists to self-report the program content that they delivered. All sessions between coaches and clients were audio-recorded. One session per client was randomly selected for fidelity assessment. Self-report checklists were assessed by one coder, and two independent coders reviewed session transcripts to assess program content fidelity. Results: On average, coaches self-reported delivering 96% of program session content, and transcript analyses indicated coaches successfully delivered 89% of program session content. Conclusion: SSBC sessions were delivered with high levels of fidelity. These findings increase confidence that SSBC program results are due to the intervention content.
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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 | 0.053 | 0.311 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".