Return to productivity following traumatic brain injury: Cognitive, psychological, physical, spiritual, and environmental correlates
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the determinants and correlates of return to productivity (RTP) defined here as return to paid employment and/or school four years following traumatic brain injury (TBI). METHOD: Participants included 46 people with TBI, part of a prospective, cohort study, and 14 friend/family member controls all employed and/or in school at time of injury or inception into the study. Variables were selected for investigation based on two models of recovery. Demographic and injury severity data including time to recover free recall were collected at time of injury, on admission to a trauma unit. Data on other variables (neuropsychological, psychological, physical, spiritual, environmental) were collected concurrent with productivity status at a mean of 4.3 years post-TBI. RESULTS: Time to recover free recall (measured acutely), neuropsychological status, pain severity, depression, and the use of maladaptive coping behaviours were all related to productivity status (p < 0.05). When these variables were entered into exploratory, planned hierarchical logistic regression models time to free recall, pain, and maladaptive coping remained in the models with depression only dropping out because of the high correlation with pain (r > 0.80). CONCLUSIONS: Injury severity (time to free recall), physical status (pain), and psychological status (depression, coping) are important to understanding differences in productivity outcomes. Addressing pain, depression and coping in rehabilitation programs may have a positive impact on 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".