Effect of de-industrialisation on working conditions and self reported health in a sample of manufacturing workers
Bibliographic record
Abstract
STUDY OBJECTIVE: To explore the impact of de-industrialisation over a 20 year period on working conditions and health among sawmill workers, in the province of British Columbia (BC), Canada. DESIGN AND SETTING: This investigation is based on a sample of 3000 sawmill workers employed in 1979 (a year before the beginning de-industrialisation) and interviewed in 1998. The sample was obtained by random selection from an already gathered cohort of approximately 28 000 BC sawmill workers. Change in working conditions from 1979 to 1998 are described. Self reported health status, in 1998, was used as a dependent variable in logistic regression after controlling for confounders. MAIN RESULTS: Downsizing in BC sawmills eliminated 60% of workers between 1979 and 1998. Working conditions in 1998 were better for those who left the sawmill industry and obtained re-employment elsewhere. Workers who remained employed in restructuring sawmills were approximately 50% more likely to report poor health than those re-employed elsewhere. CONCLUSIONS: Working conditions and health status were better for workers who, under pressure of de-industrialisation, left the sawmill industry and obtained re-employment outside this sector.
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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 | 0.000 |
| 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".