Return to work after first incidence of long-term sickness absence: A 10-year prospective follow-up study identifying labour-market trajectories using sequence analysis
Bibliographic record
Abstract
Aims: The study aim was to identify prototypical labour-market trajectories following a first incidence of long-term sickness absence (LTSA), and to assess whether baseline socio-demographic characteristics are associated with the return-to-work (RTW) process and labour-market attachment (LMA). Methods: This prospective study used Norwegian administrative registers with quarterly information on labour-market participation to follow all individuals born 1952–1978 who underwent a first LTSA during the first quarter of 2004 (n =9607) over a 10-year period (2004–2013). Sequence analysis was used to identify prototypical labour-market trajectories and LMA; trajectory membership was examined with multinomial logistic regression. Results: Sequence analysis identified nine labour-market trajectories illustrating the complex RTW process, with multiple states and transitions. Among this sample, 68.2% had a successful return to full-time work, while the remaining trajectories consisted of part-time work, unemployment, recurrence of LTSA, rehabilitation and disability pension (DP). A higher odds ratio (OR) for membership to trajectories of weaker LMA was found for females and older participants, while being married/cohabitating, having children, working in the public sector, and having a higher education, income and occupational class were associated with a lower OR of recurrence, unemployment, rehabilitation and DP trajectories. These results are consistent with three LMA indicators. Conclusions: Sequence analysis revealed prototypical labour-market trajectories and provided a holistic overview of the heterogeneous RTW processes. While the most frequent outcome was successful RTW, several unfavourable labour-market trajectories were identified, with trajectory membership predicted by socio-demographic measures.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".