The impact of temporary employment and job tenure on work-related sickness absence
Bibliographic record
Abstract
OBJECTIVES: To investigate the impact of temporary employment and job tenure on work-related sickness absence of 1 week or more. METHODS: A longitudinal analysis was undertaken of the time to work-related sickness absence from the start of a job using the Canadian Survey of Labour and Income Dynamics. The sample consisted of 4777 individuals who experienced 7953 distinct job episodes and 167 absences. There were 114,488 person-job-month observational units. The major variables of interest in this study were a variable identifying whether the job was temporary or permanent, and tenure on the job. RESULTS: Individuals in temporary jobs were as likely to have a work-related sickness absence as individuals in permanent jobs. Individuals with job tenure of 4-6 months were 64% less likely to have an absence than individuals with longer tenures. Individuals in a union were more likely to have an absence. Firm size was not associated with absence. CONCLUSIONS: Previous studies have suggested that temporary employment and job tenure are associated with work-related health risk exposures and the ability to take a sickness absence, but these studies have not considered the nature of the employment contract in a longitudinal framework. This analysis did not find temporary employment to be associated with differential absence rate after controlling for tenure, prior health status, and several other individual and job characteristics. Short tenure is negatively related to the probability of work-related sickness absence, union membership is positively related, and firm size is not related to this variable.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".