P-330 Age differences in return-to-work following injury: Understanding the role of age across longitudinal follow-up
Bibliographic record
Abstract
<h3>Introduction</h3> Older age tends to be associated with longer time to return-to-work (RTW) following a workplace injury and multiple recurrences of work absence following an initial RTW attempt. However, few studies have examined the underlying factors that are responsible for these differences. <h3>Objectives</h3> To examine the overall association between age and return-to-work (RTW), understand the extent to which functional, psychosocial, organizational, life-stage related factors indirectly explain these associations, and examine whether there is a remaining direct proportion not mediated by these factors. <h3>Methods</h3> We used survey data from a prospective cohort of injured workers in Victoria, Australia. Participants were recruited during the 2014 to 2015 period from monthly samples of claimants identified by the compensation system. Path models examined the relationship between age and RTW, and the proportion mediated via functional, psychosocial, organizational, life-stage related factors. <h3>Results</h3> Older age was associated with non-RTW, although the pattern was not observed consistently across follow-up surveys. A proportion of the overall relationship between age and non-RTW was explained by functional and life-stage factors and RTW status at previous time points. <h3>Conclusion</h3> Findings underscore the importance of moving beyond age measured only in chronological years, towards more complex conceptual and analytical models that recognize age as a multidimensional construct.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".