Counsellor-client interactions in a physical activity behavioural intervention for adults with disabilities: A State Space Grid analysis study
Bibliographic record
Abstract
BackgroundPhysical activity (PA) counselling research has mainly focused on identifying which behavior change techniques (BCTs) are delivered by a counsellor. Less is known about how BCTs are received by clients. State Space Grids (SSG) is a dynamic system method that can be used to study counsellor-client interactions by examining frequencies, durations and sequences of BCT delivery and receipt. This study demonstrates how to use SSG to characterize counsellor-client interactions during a PA behavioural support intervention for adults with disabilities. Methods This secondary data analysis study used data from adults with spinal cord injury (age: 45.79±13.63; females: n=5; males: n=9) who received PA counselling. Transcripts of 30 audio-recorded counselling sessions (total duration: ~8.3 hours) were double-coded for BCT delivery and receipt statements using a reliable coding method (>84% agreement) and analyzed using SSG methods. Results The SSG analyses revealed that frequencies, durations, and sequences of BCT delivery and receipt varied largely within and between dyads. Across all sessions, the counsellor and client spent on average 32-34% of their time on talking about BCTs related to goals and planning, ~29% of their time talking about other BCTs (e.g., self-belief, support strategies), and the remaining 27-29% of their time talking about other topics (not BCT-specific). Conclusion This study showed how dynamic system methods can be used to characterize counsellor-client interactions and illustrate the variability of how BCTs are delivered by a counsellor and received by clients in a PA behavioural support intervention. We demonstrated that insights into frequencies, durations and sequences of BCT delivery and receipt can help advance our understanding of PA behavioural support for adults with and without disabilities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".