Extending Our Understanding of the Social Determinants of Physical Activity and Sedentary Behaviors in Families: A Systems Mapping Approach
Bibliographic record
Abstract
BACKGROUND: The social environment is important to consider for effective promotion of movement behaviors like increased physical activity (PA) and reduced sedentary behavior (SB); yet, it is less often considered than individual and built environments. One way to advance social environment research is to develop system maps, an innovative, participatory, action-oriented research process that actively engages stakeholders to visualize system structures and explore how systems "work." The purpose of this research was to develop PA and SB system maps of the social environment embedded within the core/nuclear family system. METHODS: The development process began with a 2-day multicountry, 16-researcher, in-person participatory workshop in August 2023, followed by multiple online follow-up consultations. Attendees contributed to the creation of the maps through shared development of critical determinants and their causal pathways. The structure of the final maps was analyzed using network analysis methods to identify indicators of centrality, and key feedback loops and areas for potential intervention were explored. RESULTS: Key central determinants that were likely critical targets for systems intervention to produce changes in PA and SB and featured prominently in most of the reinforcing and balancing feedback loops included shared family interests, values and priorities, family logistical support, family cohesion/organization, and shared experiences. The maps also highlighted key determinants of the broader social environment external to the family. CONCLUSIONS: These system maps support current evidence on movement behaviors in family systems and socioecological theories and have the utility to galvanize future research and policy to promote PA and reduce SB.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".