19 Impact of Rehabilitation Pathway on the Prediction of Social Participation Outcome and Productivity After Traumatic Brain Injury
Bibliographic record
Abstract
Objective: Social participation (SP) represents a key goal in TBI rehabilitation, as it allows the individual to return to active and purposeful roles in the community. However, studies on predictors of SP specific to post-acute universally accessible specialized rehabilitation pathways following TBI are scarce. Our objectives were thus to 1) characterize a literature-based set of pre-injury, injury-related, and post-injury variables, as well as SP (measured with the MPAI-4 Participation scale) in individuals participating in inpatient-outpatient or outpatient rehabilitation pathways within a universally accessible and organized trauma continuum of care and 2) assess the use of these variables to predict SP outcome after TBI for each rehabilitation pathway. Participants and Methods: Participants (N = 372) were adults admitted between 2016 and 2020 to an inpatient-outpatient rehabilitation pathway or an outpatient rehabilitation pathway after sustaining a mild, moderate or severe TBI. The French-Canadian adaptation of the MPAI-4 questionnaire (Malec, 2005; Guerrette & McKerral, 2021; McKerral et al., 2014) presents three subscales (Abilities, Adjustment, Participation) and a total score, assessing functional and SP outcome after TBI. The MPAI-4 is completed for all individuals at admission and discharge in three major rehabilitation centers in the greater Montreal region. Independent variables consisted of sociodemographic and clinical characteristics collected from medical files and rehabilitation databases. Outcome measures consisted of a general SP level (MPAI-4 Participation subscale score at discharge from outpatient rehabilitation) and productivity status at discharge (productive if employed, child rearing, homemaker, student or volunteering; unproductive if unemployed or retired). Multiple and logistic regressions investigated the predictive value of each variable for SP outcome and productivity, separately for each rehabilitation pathway. Results: Samples’ sociodemographic and clinical characteristics differed between rehabilitation pathways. The inpatient-outpatient sample presented older age, lower productivity before TBI and poorer SP levels at admission and discharge from rehabilitation. However, both samples showed significant improvement on SP levels during rehabilitation. For the inpatient-outpatient rehabilitation path, general SP outcome was significantly predicted by three variables (education years, MPAI-4 Ability and Adjustment scores at rehabilitation intake; R2 = 49%), whereas productivity status at discharge was significantly predicted by age at time of injury (R2 = 72%). For the outpatient rehabilitation path, general SP outcome was significantly predicted by five variables: premorbid hypertension, mental health diagnosis, total indirect rehabilitation hours received, MPAI-4 Abilities and Adjustment scores at rehabilitation intake (R2 = 47%), while productivity status at discharge was significantly predicted by age at time of injury and education years (R2 = 44%). Conclusions: Different TBI rehabilitation care pathways showed distinct sample characteristics, as well as different premorbid and post-injury predictors of SP outcome and productivity. This highlights the importance of being aware of the potential limited generalizability of previously identified predictors when extrapolated to different clinical, rehabilitation and sociocultural contexts. The predictive models obtained could help clinicians identify more accurately patients at risk of showing poorer SP and productivity outcomes at end of rehabilitation, influence intervention approaches put forward with these individuals and set more appropriate goals and expectations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".