Rehabilitation Utilization following a Work-Related Traumatic Brain Injury: A Sex-Based Examination of Workers’ Compensation Claims in Victoria, Australia
Bibliographic record
Abstract
OBJECTIVES: To report on and examine differences in the use of four types of rehabilitation services (occupational therapy, physiotherapy, psychology, and speech therapy) by men and women following a work-related traumatic brain injury in Victoria, Australia; and to examine the importance of demographic, need, work-related and geographic factors in explaining these differences. METHODS: A retrospective cohort design was used to analyze 1786 work-related traumatic brain injury workers' compensation claims lodged between 2004 and 2012 in Victoria, Australia. ZINB regressions were conducted for each type of rehabilitation service to examine the relationship between sex and rehabilitation use. Covariates included demographic, need-related, work-related, and geographic factors. RESULTS: Out of all claims (63% male, 37% female), 13% used occupational therapy, 23% used physiotherapy, 9% used psychology, and 2% used speech therapy at least once during the first year of service utilization. After controlling for demographic, need-related, work-related, and geographic factors, women were more likely to use physiotherapy compared to men. Men and women were equally likely to use occupational therapy and psychology services. The number of visits in the first year for each type of service did not differ between male and female users. CONCLUSIONS: Our findings support a sex-based approach to studying rehabilitation utilization in work-related populations. Future research is needed to examine other factors associated with rehabilitation utilization and to determine the implications of different rehabilitation utilization patterns on health and return-to-work outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".