Health and Quality of Life of Bangladeshi Migrants in Melbourne—An Analysis with Four Multi-Attribute Utility and Three Subjective Wellbeing Instruments
Bibliographic record
Abstract
The aim is to investigate the health and quality of life (QoL) of Bangladeshi migrants using 7 Multi-Attribute instruments. Participants for this empirical study comprised Bangladeshi migrants living in Melbourne. Data were collected through a questionnaire survey. Respondents who completed the questionnaire were aged between 18 and 65 years old. Over 50% of the participants possessed excellent or very good health and 83% did not have any significant illness. Both males and females were found to be more overweight but less obese compared with the Australian population. Over 70% had low and 13% had high or very high levels of psychological distressas measured by the K10. The lifestyle of the migrants is distinct—about 80% never drank alcohol or smoked cigarettes. The recently developed AQoL-8D was the most sensitive to psychological distress, the personal wellbeing index and with BMI and had the highest correlation with EQ-5D and SF-6D within MAU instruments. Individual utility scores varied significantly at the individual level. The significant loss of QoL with increasing obesity and psychological distress are areas of concern for policy makers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".