Development of the social dimensions of health behaviour framework
Bibliographic record
Abstract
Despite rapid theoretical expansion in conceptualising individual and environmental processes, the examination of social processes associated with health behaviours has a less cohesive theoretical landscape. The purpose of this mapping review and content analysis was to develop a taxonomy of social dimensions applicable to health behaviours. Michie et al. (2014) ‘ABC of Theories of Behaviour Change’ text, which includes 83 behaviour change theories, was used as the data-set, whereby an iterative concurrent content analysis was undertaken with respect to all relational/interpersonal psychological dimensions. The analysis resulted in a social dimensions of health behaviour (SDHB) framework of 10 dimensions, including seven sub-types of social appraisal dimensions and three-sub-types of social identification dimensions. The SDHB revealed that specific dimensions, such as descriptive norm, are prevalent in behavioural theories, while other dimensions have seen less attention. Further, while most social constructs in behavioural theories are represented by only one social dimension in the SDHB, other constructs have complex representation. This version 1.0 of the SDHB framework should assist in specifying the core social dimensions in health behaviour, provide a common lexicon to discuss relational constructs in psychological theories, amalgamate the disparate social constructs literature and identify opportunities for further research to advance theory development and 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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".