Bibliographic record
Abstract
In the mid-1990s, an explosion of behavioral theory testing in the physical activity domain began and has continued at a strong pace, as researchers attempt to explain why some people are active and others are not. Theory of planned behavior, social cognitive theory, and the transtheoretical model have comprised the bulk of this research on theory testing (9), yet recent reviews and meta-analyses have not been favorable for their utility in physical activity behavior change efforts (2,7,11). These original theories, applied from social psychology and allied disciplines, also have shown some limitations in terms of scope and missing variables important to physical activity. Thus, recent calls have been made to adapt and create the next generation of models that may better serve physical activity behavior (5,9,11). The Integrated Behavior-Change (IBC) Model for Physical Activity proposed by Hagger and Chatzisarantis (4) takes up this challenge by offering a blend of the theory of planned behavior and self-determination theory under careful consideration of previous limitations of both these theories. Avoiding construct redundancy is a major challenge in theory integration, as many theories use different labels for similar concepts (1). Hagger and Chatzisarantis have performed this task admirably in their IBC Model, as the constructs of the theory of planned behavior have replaced the self-determination theory’s regulation constructs, which are somewhat redundant with the theory of planned behavior and less advanced/distinct in terms of past theory testing. This creates a basis for theory of planned behavior cognitions via the self-determination theory’s need for autonomy while using the depth of the theory of planned behavior’s structure as proximal antecedents of physical activity. The IBC Model, like several other next-generation models (6,8,10,12), also uses a phased structure (motivational, volitional) and considers the role of deliberative and implicit determination of physical activity that has been a shortcoming of the early physical activity theories. There still are some areas where the IBC may be further refined. For example, the model may lack consideration of the affective domain in physical activity behavior over reasoned approaches to motivation (3). The self-determination theory’s intrinsic regulation construct, omitted from their model, comprised this affective domain better than the general attitude construct within the theory of planned behavior. Furthermore, the role of social-ecological context goes unmentioned, and the implicit constructs within the IBC Model have a relatively underdeveloped antecedent structure in comparison with the deliberative constructs. Still, Hagger and Chatzisarantis are open to modification of the model if researchers can establish an overlooked explanatory process or mechanism. This suggests that the IBC Model can be viewed as a master template, which is very helpful to ongoing theory testing. Overall, the model has had successful tests to support its structure via mediation of autonomy to behavior using passive designs. As the authors point out, the critical next step will be to establish its utility in behavior change — a feature within its title — and overcome the applications of early models, like the theory of planned behavior, that failed to gain traction with experimental tests. Ryan E. Rhodes Behavioural Medicine Laboratory Faculty of Education University of Victoria, Victoria British Columbia, Canada R.E. Rhodes is supported by a Canadian Cancer Society Senior Scientist Award and through funds from the Social Sciences and Humanities Research Council of Canada, The Canadian Cancer Society, and the Canadian Institutes for Health Research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".