Use of dynamic systems methods to characterize dyadic interactions in smoking cessation behavioural support sessions: A feasibility study
Bibliographic record
Abstract
BACKGROUND: Understanding how behaviour change techniques (BCTs) operate in practice requires a method for characterizing the reciprocal, dynamic, and real-time nature of behavioural support interactions between practitioners and clients. State space grids (SSGs) are an observational, dynamic systems methodology used to map the trajectory of dyadic interactions in real time. By mapping the flow of events in terms of practitioner and client actions, SSGs are potentially well suited to characterize behavioural support sessions. PURPOSE: To develop reliable methods and examine the feasibility of using the SSG methodology for characterizing practitioners' delivery of and clients' response to BCTs in smoking cessation behavioural support sessions. METHODS: Smoking cessation behavioural support sessions were video-recorded and transcribed verbatim (n = 6 recordings; 2,916 statements). All speech was coded independently by two researchers for content and duration using published frameworks for specifying practitioner-delivered and client-received BCTs in smoking cessation interactions. Inter-rater reliability was assessed. Indices of practitioner-client interaction dynamics were derived: (1) reciprocity (i.e., attractor states, content congruence, conditional pairing) and (2) temporal patterning (i.e., variability, inter-grid distance, combinatory micro-patterning, sessional macropatterning). The extent to which indices can describe differences between sessions involving different practitioners and clients was examined. RESULTS: Inter-rater reliability was moderate at 72% agreement. Indices of reciprocity and temporal patterning characterized differences between sessions involving different practitioners and clients. CONCLUSIONS: State space grids provide a method for characterizing the complexity and variability of practitioner-delivered and client-received BCTs in behavioural support sessions. This method has potential to add explanatory value to smoking cessation intervention outcomes. Statement of Contribution What is already known on this subject? Frameworks exist for characterizing practitioner-delivered and client-received behaviour change techniques (BCTs). Methods are still needed to investigate which BCTs are effective under what conditions. State space grids (SSGs) are a dynamic systems method that may better characterize behavioural support interactions. What does this study add? First reliable, dynamic systems, SSG coding procedures, methods, and measures to characterize behavioural support. A method for examining reciprocality and temporal patterning of BCT delivery and receipt. Establishes a dynamic systems method that adds 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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".