Social Network Negativity and Physical Activity: New Longitudinal Evidence for Young and Older Adults 2015-2018
Notice bibliographique
Résumé
Social Network Negativity and Physical Activity: New Longitudinal Evidence for Young and Older Adults 2015-2018 Soli D. Dubash. Department of Sociology, University of Toronto, Canada Markus H. Schafer Department of Sociology, Baylor University, USA The Version of Record of this article has been published and is available in Research Quarterly for Exercise and Sport, 27 Jun 2023, and can be found here: https://www.tandfonline.com/doi/full/10.1080/02701367.2023.2205910 Acknowledgements: We thank Blair Wheaton, Scott Schieman, Melissa Milkie, Chris Smith, and Josée Johnston for their comments on an earlier draft of this study. Declaration of interest statement: The research presented in this paper is that of the authors and does not reflect the position of the funding sources. The first author is funded in part by the Social Sciences and Humanities Research Council Joseph-Armand Bombardier Canada Doctoral Graduate Scholarship (grant # 767-2020-1225), and the University of Toronto. The funding sources did not have any role in the study design; collection, analysis, and interpretation of data; writing of the report; or the decision to submit the report for publication. No financial disclosures were reported by the authors of this paper. Abstract Purpose: Physical activity (PA) has considerable public health benefits. Positive aspects of the interpersonal environment are known to affect PA, yet few studies have investigated whether negative dimensions also influence PA. This study examines the link between changing social network negativity and PA, net of stable confounding characteristics of persons and their environments. Method: Polling respondents in the San Francisco Bay Area over three waves (2015-2018), the UCNets project provides a panel study of social networks and health for two cohorts of adults. Respondents were recruited through stratified random address sampling, and supplemental sampling was conducted through Facebook advertising and referral. With weights, the sample is approximately representative of Californians aged 21-30 and 50-70. Personal social networks were measured using multiple name-generating questions. Fixed effects ordered logistic regression models provide parameter estimates. Results: Younger adults experience significant decreases in PA when network negativity increases, while changes in other network characteristics (e.g., support, size) did not significantly predict changes in PA. No corresponding association was found for older adults. Results are net of baseline covariate levels, stable social and individual differences, and select time-varying characteristics of persons and their environments. Conclusion: Leveraging longitudinal data from two cohorts of adults, this study extends understanding on interpersonal environments and PA by considering the social costs embedded in social networks. This is the first study to investigate how changes in network negativity pattern PA change. Interventions which help young adults resolve or manage interpersonal conflicts may have the benefit of helping to promote healthy lifestyle choices. Keywords: Physical Activity, Interpersonal Relations, Negative Ties, Relationship Change
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».