Return-to-work challenges following a work-related mild TBI: The injured worker perspective
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To explore how individuals with work-related mild traumatic brain injury (wrMTBI) experience return-to-work (RTW) processes when returning to the workplace where the injury occurred. DESIGN: RTW experiences were explored using in-depth interviews and an inductive analytic approach. Qualitative analysis guided by the research question moved through phases of line-by-line and thematic coding through which categories and the interaction between categories emerged. PARTICIPANTS: Twelve workers diagnosed with a wrMTBI reported on their RTW experiences following wrMTBIs that occurred 3-5 years prior to the time of the interview. MAIN OUTCOMES AND RESULTS: Participants perceived employer and workers' compensation factors as profoundly influencing their RTW experiences. Participants consistently reported that employers and workers' compensation representatives had an inadequate understanding of wrMTBI sequelae. Six of 12 participants were re-injured following their wrMTBI, with three of these injuries occurring at work. CONCLUSION: Employers, co-workers and workers' compensation representatives should be aware of wrMTBI sequelae so injured workers can receive appropriate supports and both stigmatization and re-injury can be mitigated. Greater attention to the structural and social elements of workplace and compensation environments could inform strategies to break down barriers to successful return-to-work following a wrMTBI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".