Social Cohesion And Strong Social Bonds In Communities: The Missing Link In Promoting An Active-lifestyle
Bibliographic record
Abstract
PURPOSE: Physical inactivity is a significant and modifiable risk factor for multiple chronic conditions including reduced brain health and cognitive decline. Yet nearly 85% of the world’s population is sedentary. More effective strategies to promote physical activity (PA) are urgently needed. Programs aimed at behavioral change show effectiveness mostly in the short-term and do not sustain active lifestyles. However, recent studies show that communities with strong social bonds are likely to provide safe and accessible spaces for recreational PA. Therefore, promoting PA together with strengthening community social cohesion may significantly improve PA participation. METHODS: We performed a narrative review of publications on brain-health benefits of PA, community cohesion and health promotion. The authors searched MEDLINE, PsycINFO, EMBASE, Scopus, CINAHL, and EBSCO. Studies exploring the impact of PA on the brain were evaluated. Additionally, publications on interventions for promotion of PA, public health, and public policies were explored. RESULTS: Studies demonstrated that moderate intensity PA increases cerebral flow, oxygen extraction, and neural metabolism. These processes initiate additional events which promote neuronal health, involving enhancement of neurotrophic factors and balanced release of neurotransmitters necessary for brain health and function. Moreover, social networks and social cohesion serve as influential mediums for disseminating information, shaping attitudes, and fostering collective engagement in PA. Thus, communities with strong social bonds are more likely to provide safe and accessible space for recreational activities. Public policy can ensure such environments are created and sustained. CONCLUSIONS: PA advances brain function by multiple pathways. Additionally, communities with high social cohesion promote PA participation. Well-designed policies can facilitate the development of infrastructure, such as parks and recreational facilities; incentivize community programs encouraging physical activity; and promote health-management behaviors. By recognizing the interplay between social networks, social cohesion, and public policy, we can create environments that empower individuals to embrace and maintain physically active lives.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".