Characterizing clients' verbal statements in behavioural support interventions: The case of smoking cessation
Bibliographic record
Abstract
BACKGROUND: Reliable methods have been developed for characterizing behavioural interventions in terms of component practitioner-delivered behaviour change techniques (BCTs). As yet, no corresponding methods have been developed for characterizing client responses. PURPOSE: To develop a method for characterizing clients' verbal statements in audio-recordings of smoking cessation behavioural support consultations. METHODS: An established framework for specifying practitioner-delivered BCTs was adapted to account for corresponding BCTs in clients' verbal statements. A total of 1,429 client statements within 15 transcripts of audio-recorded consultations were independently coded using the framework. RESULTS: Of the 58 BCT categories in the practitioner framework, 53 corresponding client BCTs were included and five codes unrelated to smoking cessation were added. Forty client BCTs were reliably identified at least once across sessions (75.1% agreement; PABAK = .77). CONCLUSIONS: It is possible to reliably categorize clients' verbal statements in smoking cessation consultations in terms of responses to BCTs delivered by the practitioner. When used alongside the taxonomy of practitioner-delivered BCTs, this method could provide a basis for investigating the dyadic interaction between the practitioner and client. Statement of contribution What is already known on this subject? Taxonomies exist for characterizing practitioner-delivered behaviour change techniques (BCTs) in interventions. Client responses and contributions are an important part of the behaviour change process. Examining clients' receipt of BCTs can add explanatory value to the outcomes of interventions. Current taxonomies and methods do not characterize client responses in terms of BCTs. What does this study add? First method to characterize clients' statements corresponding to a practitioner BCT taxonomy. Provides a method for investigating the dyadic practitioner-client interaction using BCTs. A method that has potential to add explanatory value to the outcomes of interventions.
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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 | 0.039 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".