The Relationship Between Socioeconomic Status and Physical Activity Among Adolescents
Bibliographic record
Abstract
Background: Many Canadian youth are inactive (Active Healthy Kids Canada, 2009). Socioeconomic status (SES) is one factor that influences youth physical activity (PA) levels; however, the factors involved in this relationship are not well understood. Given that numerous quantitative studies suggest there is a positive relationship between SES and PA among adolescents, but offer little insight into why the given relationship exists, the purpose of the current thesis is to study the factors that influence the relationship between SES and PA among youth aged 12–14 years through a qualitative lens.\nMethods: Low (n = 2) and high SES (n = 3) parents and community support liaisons (n = 15) took part in one-on-one interviews to provide insight into how various SES indicators (i.e., income, education, and occupation) influence adolescent PA. The interviews were transcribed verbatim, coded into meaning units, and put into themes that related to smaller sub-themes.\nResults: The three major themes were access, time, and awareness and related to the indicators of income, occupation, and education, respectively. For each theme specific sub-themes were identified. Access is related to one’s income which impacts adolescent PA by determining whether a parent/family: 1) has money available to cover the direct costs of PA programs, 2) has transportation and travel options available for PA programming, 3) is able to meet basic needs with limited stress, and 4) is eligible to access subsidies or low cost programs. Time is the theme related to one’s occupation which impacts one’s employment schedule. Awareness is related to both formal and informal education which impacts one’s: 1) knowledge of the importance of PA and, 2) access to resources and exposure to PA, and 3) knowledge of subsidies and low cost programs. Other social-ecological factors also emerged from the data.\nConclusion: The relationship between SES and PA among adolescents is complex; however, the qualitative nature of this study allowed an in-depth analysis of participant’s experiences in order to better understand the factors that influence this relationship. The current results provide insight into factors that can be targeted in future PA interventions aiming to equalize PA opportunities between adolescents of varying levels of SES. Although income is related to the greatest number of sub-themes, targeting factors based on occupation and education is also warranted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".