SP6-12 The health and well-being of laid-off automobile industry workers in Durham, Canada
Bibliographic record
Abstract
Introduction The City of Oshawa in Durham, Canada is a major manufacturing hub for automotive production. In 2008–2009, we witnessed an unprecedented economic crisis not seen since the great depression. A record number of auto-workers lost their jobs as a result. Little is known about how these lay-offs affected the health and well-being of the workers. This exploratory study examined the impact of being laid-off on the emotional, physical, social and financial health of auto-workers. Methods A purposive sampling technique was employed to recruit participants from two locations: The CAW Community Action Centre and a Service Fair organised by the Durham Region Local Training Board. All participants were asked to complete an in-depth demographic and health questionnaire. Results A total of 36 laid-off workers were interviewed between 28 October and 30 November 2009. Approximately two-third of our participants were male and the mean age was 45 (SD=6, range=30–61). The average length of time since laid-off was 13 months (SD=8, range=1–36). Half of our participants reported a feeling of burden to others and a loss of social status, and 75% reported a loss of identity and pride. With regard to the self-rated health and well-being status on a scale of 1 (very poor) to 5 (excellent) since being laid-off, our participants reported mid-level physical health (score=3.12), but relatively poor emotional health (score=2.59), social health (score=2.5) and financial health (score=1.97). Conclusion Job loss can have a wide range of effect on one's well-being, including physical, emotional, social and financial health.
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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 | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".