Predictors of Self-Reported Health Status of Ready-made Garment Workers in Bangladesh in Post Global Economic Recession Period
Bibliographic record
Abstract
Introduction There has been disagreement within academia in Bangladesh on whether the global economic recession of 2008-2009 came out as a bane or a boon to their economy and for their people, particularly workers in the ready-made garments (RMG) sector; therefore, we sought to conduct a study among currently employed and recently unemployed RMG workers to examine the influence of recession on their self-reported health status. Methods This cross-sectional study was conducted among 200 workers across 20 factories and 108 recently unemployed workers from different locations of Dhaka. Workers were selected based on a systematic sampling method from 20 randomly selected factories. Unemployed respondents were selected via snowball sampling. A questionnaire was prepared to cover different socio-demographic variables, which were then explored against an outcome variable of how the respondents rate their current health status (2009) compared with their past health status during the economic recession period (2008). A simple logistic regression was conducted for each of the independent variables with the outcome variable. Finally, all independent variables were loaded against the outcome variable, and multiple logistic regression was run. Results The only statistically significant predictor of self-reported health status was age, which indicated a 4% decrease (p = 0.05; 95% confidence interval (CI), 0.9203417 to 1.000015) in improved or better health with each year increase in age, holding other variables constant. Respondent health status was unchanged or even improved after the period of recession. The employed group had 1542.061 Taka (approximately $20) more average monthly family income than the unemployed group (two-sample t-test p-value 0.007), their health status was not affected (odds ratio (OR) 0.998; p-value 0.907). Conclusion The absence of an association between self-reported health status and economic recession is not uncommon, and explanations have been proposed for this phenomenon.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".