Disparities in physical activity descriptive norms: the case of immigrants and racial/ethnic minorities in New York City
Bibliographic record
Abstract
Descriptive norms – conceptualized as the tendency for individuals to initiate given behaviours because significant others are engaged – have been considered helpful in increasing the uptake of physical activity. Yet, the literature pays little attention to the attainment of descriptive norms among the populations with inadequate levels of physical activity such as racial/ethnic minorities and immigrants. Using a representative survey from New York City, this study aimed to address this void. We found at the bivariate level that immigrants and racial/ethnic minorities (i.e., Black, Hispanic, and Asian) had lower levels of descriptive norms than their native-born and White counterparts. Importantly, such disparities were completely attenuated once socioeconomic status was controlled for, except for Asian Americans. Based on these findings, we provided some policy implications. First, intervention programs may need to prioritize the integration of minority populations. Second, there is the urgent need to create avenues for the social mixing of immigrants and racial/ethnic minorities, which may provide them opportunities to interact and network with those who actively engage in physical activity. Finally, it is important to reduce socioeconomic disparities between foreign-born and native-born populations as well as racial/ethnic minorities and their white counterparts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".