Determinants and Economic Consequences of Self Reported Illness Among Indian Construction Workers – A Multicentre Study
Bibliographic record
Abstract
Abstract Background Construction workers have health hazards inherent to the nature of work and at further risk for poverty due to poor living conditions. We investigated perceived illness among workers and family members in the past year and the economic consequences of morbidities in terms of Catastrophic Health Expenditure (CHE). Methods In this cross-sectional multicenter study, we recruited construction workers of both sexes from construction sites of two Indian cities. We collected details on illnesses requiring a healthcare visit in the past year, expenditure and related details. Results Of 1263 participants recruited, data on illness during the past year were reported by 1110 participants; 37% (n = 302) reported illness among themselves or family members requiring a healthcare visit. We constructed a regression model to ascertain demographic and living condition determinants of illness (R^2 = 54%, p < 0.001). We observed kitchen in the living space (OR = 1.87), and using unhygienic smoky cooking fuels (OR = 1.87) were associated with an increased likelihood of reporting illness. More than a quarter of those who reported illness incurred CHE. Both CHE incurred and non-incurred groups displayed similar trends of health-seeking behaviors. Conclusion We conclude that both prevalence of self reported illness and CHE were relatively high, especially among the migratory group. Our results demonstrate that poor living conditions add to the burden of morbidity in construction workers and families. Providing medical coverage for this population vulnerable to economic hardships, engaging and educating about affordable healthcare are important future steps to prevent further economic consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".