Association between neighborhood socioeconomic status and screen time among pre-school children: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Sedentary behavior is considered a separate construct from physical activity and engaging in sedentary behaviors results in health effects independent of physical activity levels. A major source of sedentary behavior in children is time spent viewing TV or movies, playing video games, and using computers. To date no study has examined the impact of neighborhood socioeconomic status (SES) on pre-school children's screen time behavior. METHODS: Proxy reports of weekday and weekend screen time (TV/movies, video games, and computer use) were completed by 1633 parents on their 4-5 year-old children in Edmonton, Alberta between November, 2005 and August, 2007. Postal codes were used to classified neighborhoods into low, medium or high SES. Multiple linear and logistic regression models were conducted to examine relationships between screen time and neighborhood SES. RESULTS: Girls living in low SES neighborhoods engaged in significantly more weekly overall screen time and TV/movie minutes compared to girls living in high SES neighborhoods. The same relationship was not observed in boys. Children living in low SES neighborhoods were significantly more likely to be video game users and less likely to be computer users compared to children living in high SES neighborhoods. Also, children living in medium SES neighborhoods were significantly less likely to be computer users compared to children living in high SES neighborhoods. CONCLUSIONS: Some consideration should be given to providing alternative activity opportunities for children, especially girls who live in lower SES neighborhoods. Also, future research should continue to investigate the independent effects of neighborhood SES on screen time as well as the potential mediating variables for this relationship.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".