Measuring behavior change technique delivery and receipt in physical activity behavioral interventions.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: Research on physical activity behavioral support has mainly focused on measuring the absence or presence of behavior change techniques (BCTs) delivered by a counselor. We present a method to measure BCT delivery and receipt in physical activity behavioral support interventions. RESEARCH METHOD/DESIGN: The method was developed and tested using transcripts from behavior change counseling sessions delivered as part of a theory- and evidence-based physical activity intervention for adults with disabilities. Using existing methods, a new method was developed to code counselor and clients' verbal statements (BCTs and other statements). Two coders independently coded 30 transcripts of audio-recorded counseling sessions. Interrater reliability was assessed using percentage agreement and Prevalence Adjusted Bias Adjusted Kappa (PABAK). RESULTS: (counselor:13%, client:24%). CONCLUSIONS/IMPLICATIONS: This study presents a reliable coding method to measure BCT delivery and receipt in physical activity behavioral support interventions. The method can be used to enhance intervention fidelity assessment and study interactions between counselors and clients with and without disabilities. Measuring and evaluating BCT delivery and receipt can provide new insights into what types of behavioral support work best under which circumstances. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".