Vocational Services for Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To examine characteristics and diversity among vocational treatment services in model programs for traumatic brain injury (TBI) rehabilitation. SETTING: Vocational or postacute treatment components of 16 TBI Model System (TBIMS) centers. PARTICIPANTS: Vocational director/coordinator from each TBIMS surveyed in semistructured phone interview. MEASURE: Survey of vocational services for people with TBI, with about 100 closed and open-ended questions on vocational assessments; pre- and postjob placement treatments; program philosophies; funding; and integration of cognitive, behavioral, family, and medical rehabilitation interventions. RESULTS: Great diversity was found among the vocational services of the 16 TBIMS. Programs fell into 3 clusters emphasizing medical rehabilitation services, supported employment, or a combination of these with an emphasis on case management. Job coaching was identified as a key intervention, but there was great variability in intensity, availability, and funding of coaching services. CONCLUSION: Diversity in vocational services appears related to funding differences and "parallel evolution" rather than strong treatment philosophy or scientific evidence base. Multicenter research on effectiveness or establishment of best practices in vocational rehabilitation after TBI must deal with substantial existing variability in treatment models and specific interventions, and must examine the relationship of treatment variations to case-mix factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".