Are Australian immigrants at a risk of being physically inactive?
Bibliographic record
Abstract
BACKGROUND: We examined whether physical activity risk differed between migrant sub-groups and the Australian-born population. METHODS: Data were drawn from the Australian National Health Survey (2001) and each resident's country of birth was classified into one of 13 regions. Data were gathered on each resident's physical activity level in the fortnight preceding the survey. Multivariable logistic regression, adjusted for potential confounders examined the risk of physical inactivity of participants from each of the 13 regions compared to the Australian-born population. RESULTS: There was a greater prevalence of physical inactivity for female immigrants from most regions compared to male immigrants from a like region. Immigrants from South East Asia (OR 2.04% 95% CI 1.63, 2.56), Other Asia (OR 1.53 95% CI 1.10, 2.13), Other Oceania (1.81 95% CI 1.11, 2.95), the Middle East (OR 1.42 95% CI 0.97, 2.06 [note: border line significance]) and Southern & Eastern Europe are at a significantly higher risk of being physically inactive compared to those born in Australian. In contrast, immigrants from New Zealand (OR 0.77 95% CI 0.62, 0.94), the UK & Ireland (OR 0.82 95% CI 0.73, 0.92), and other Africa (OR 0.69 95% CI 0.51, 0.94) are at a significantly lower risk of being physically inactive compared to the Australian born population. CONCLUSION: Future research identifying potential barriers and facilitators to participation in physical activity will inform culturally sensitive physical activity programs that aim to encourage members of specific regional ethnic sub-groups to undertake physical activity.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".