Present knowledge and future directions: Musings on GMCB interventions
Bibliographic record
Abstract
Much of the research on group-mediated cognitive behavioural interventions (GMCB) suggests that they are effective at increasing physical activity (PA) adherence, improving physical function, and enhancing social cognitive outcomes across diverse populations (e.g., sedentary older adults, postnatal mothers, and spinal cord injury patients). The use of cognitive-behavioural and group dynamics models to produce social-cognitive and behavioural change follows the recommendations of Cartwright (1951), Bandura (1997), Meichenbaum & Turk, (1987) and the PA intervention literature (e.g., Artinian et al, 2010). Despite GMCB's potential as a PA intervention, questions remain regarding its widespread application. For example, is the GMCB amenable to knowledge translation (KT), the dynamic process by which research findings are integrated into everyday practice? Glasgow and Emmons (2007) suggest potential barriers to successful KT implementation of health-related interventions fall under three categories: (1) intervention characteristics, (2) characteristics of the target setting, and (3) research design. GMCB KT will be discussed relative to these. Some GMCB characteristics make it attractive for implementation, but do barriers constrain this potential? Feasibility of the GMCB model to overcome common KT barriers will be examined. Directions for future GMCB research will be offered (e.g., measuring cohesion/collaboration; persistence).Acknowledgments: Supported by Canada Research Chair Training Fund
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.014 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".