The relationship between unemployment, technological change and psychosocial work conditions in British Columbia sawmills
Bibliographic record
Abstract
In the early 1980s, the sawmill sector in British Columbia (BC), Canada experienced economic recession which was followed by industrial restructuring in many mills. This is an investigation of the relationship between unemployment, which occurred in the recession and the new psychosocial and physical work conditions in restructured workplaces using a cohort of approximately 29,000 sawmill workers in 14 BC sawmills. The purpose of this investigation is to describe the changes in the cohort's job structure and labour demography in order to best frame future health studies on the effects of unemployment and restructuring. Downsizing reduced the number of workers by 60% and the number of job titles by 25%. The youngest workers were downsized and job titles with the least control and most demand were eliminated by restructuring. Although psychosocial conditions of work (assessed using 'expert' raters) improved after restructuring, these better work conditions were available to fewer workers. Four main lessons were learned. First, to assess the health impact of the downsizing/restructuring process a population-based approach must be taken with long-term follow up of downsized workers through their period of unemployment and re-employment in similar or new industries. Second, particular attention must be paid to the long-term employment experiences and associated health outcomes for downsized workers under 35 years of age. Third, although all job categories showed increased levels of control in restructured workplaces, the gradient in control across job categories was steeper in 1997 compared to 1965 which may have health implications particularly for unskilled workers in restructured mills. And, fourth, the expert rater method for estimating work conditions should be improved upon and tested by using self-reports in future health studies with this cohort.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".