Characterizing Short-Term Jobs in a Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Work histories generally cover all jobs held for ≥1 year. However, it may be time and cost prohibitive to conduct a detailed exposure assessment for each such job. While disregarding short-term jobs can reduce the assessment burden, this can be problematic if those jobs contribute important exposure information towards understanding disease aetiology. OBJECTIVE: To characterize short-term jobs, defined as lasting more than 1 year, but less than 2 years, in a population-based study conducted in Montreal, Canada. METHODS: In 2005-2012, we collected work histories for some 4000 participants in a case-control study of prostate cancer. Overall, subjects had held 19 462 paid jobs lasting ≥1 year, including 3655 short-term jobs. Using information from interviews and from the Canadian Classification and Dictionary of Occupations, we characterized short-term jobs and compared them to jobs held ≥2 years. RESULTS: Short-term jobs represented <4% of subjects' work years on average. Forty-five per cent of subjects had at least one short-term job; of these, 49% had one, 24% had two, and 27% had at least three. Half of all short-term jobs had been held before the age of 24. Short-term jobs entailed more often exposure to fumes, odours, dust, and/or poor ventilation than longer jobs (17 versus 13%), as well as outdoor work (10 versus 5%) and heavy physical activity (16 versus 12%). CONCLUSIONS: Short-term jobs occurred often in early careers and more frequently entailed potentially hazardous exposures than longer-held jobs. However, as they represented a small proportion of work years, excluding them should have a marginal impact on lifetime exposure assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".