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Record W4401226347 · doi:10.1186/s12889-024-19427-4

An investigation into social determinants of health lifestyles of Canadians: a nationwide cross-sectional study on smoking, physical activity, and alcohol consumption

2024· article· en· W4401226347 on OpenAlexaboutno aff
Xiangnan Chai, Yongzhen Tan, Yanfei Dong

Bibliographic record

VenueBMC Public Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersGovernment of Jiangsu Province
KeywordsBiostatisticsSocial determinants of healthEnvironmental healthPublic healthHealth promotionSocioeconomic statusMedicinePsychological interventionHealth equityGerontologyPopulationPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Health lifestyles exert a substantial influence on the quality of everyday life, primarily affecting health maintenance and enhancement. While health-related practices during the COVID-19 pandemic may have positively altered the health lifestyles of Canadians to a certain degree, government reports indicate that issues related to health behaviors, such as cigarette smoking, physical inactivity, and alcohol consumption, continue to pose challenges to the health of Canadians. Social determinants of these health behaviors thus hold significant academic value in the formulation of policy guidelines. OBJECTIVE: The aim of this study is to scrutinize the social determinants of health with respect to social factors that have may have impacts on the health-related behaviors of Canadians. We tested health behaviors including cigarette use, alcohol consumption, and participation in physical exercise, which are integral to the promotion and improvement of individual health. METHODS: To examine the social determinants of Canadians' health lifestyles, we utilized nationally representative data from the 2017-2018 Canadian Community Health Survey annual component. Our data analysis involved the bootstrapping method with two-level mixed-effect logistic regressions, ordered logistic regressions, and negative binomial regressions. Additionally, we conducted several robustness checks to confirm the validity of our findings. RESULTS: The findings show that demographic background, socioeconomic status, social connections, and physical and mental health conditions all play a role in Canadians' smoking, physical activity, and drinking behaviors. Noticeably, the association patterns linking to these social determinants vary across specific health lifestyles, shedding light on the complex nature of the social determinants that may influence young and middle-aged Canadians' health lifestyles. Moreover, in the context of Canada, the health-region level demographic, socioeconomic, and working conditions are significantly linked to residents' health lifestyles. CONCLUSIONS: Investigating the social determinants of health lifestyles is pivotal for policymakers, providing them with the necessary insights to create effective interventions that promote healthy behaviors among specific demographic groups. It is recommended that health education and interventions at the community level targeting smoking, physical inactivity, and alcohol consumption be introduced. These interventions should be tailored to specific subgroups, considering their demographic and socioeconomic characteristics, social networks, and health status. For instance, it is imperative to focus our attention on individuals with lower educational attainment and socioeconomic status, particularly in relation to their smoking habits and physical inactivity. Conversely, interventions aimed at addressing alcohol consumption should be targeted towards individuals of a higher socioeconomic status. This nuanced approach allows for a more effective and tailored intervention strategy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.160
GPT teacher head0.472
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2024
Admission routes1
Has abstractyes

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