Multiple psychiatric diagnoses and return-to-work following posttraumatic stress injury rehabilitation
Bibliographic record
Abstract
BACKGROUND: Posttraumatic stress injury (PTSI) is a term used to describe a range of psychiatric difficulties which arise following exposure to a psychologically traumatic event. The impact of being diagnosed with multiple psychiatric conditions on the return-to-work (RTW) outcomes of individuals with PTSI has not been adequately researched. OBJECTIVE: The current study examined whether the presence of two or more psychiatric conditions occurring simultaneously is predictive of RTW outcomes in workers with PTSI. METHOD: A population-based cohort design was conducted using archival data from injured workers admitted to a PTSI rehabilitation program. Differences in RTW outcomes and demographic, administrative, and clinical variables were compared between individuals with single and multiple psychiatric diagnoses. A range of variables were entered into a multivariable logistic regression model predicting RTW. RESULTS: The final logistic regression model indicated workers had higher odds of RTW if they had a single psychiatric diagnosis (Adjusted Odds Ratio (AOR) 2.20), non-elevated scores on a measure of traumatic stress (AOR 1.85), and reported higher self-perceived readiness to RTW (AOR 1.24). CONCLUSION: Being diagnosed with multiple psychiatric conditions appears to be associated with more negative RTW outcomes following PTSI rehabilitation.
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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 | 0.006 |
| 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 | 0.001 |
| 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".