19 Impact of Rehabilitation Pathway on the Prediction of Social Participation Outcome and Productivity After Traumatic Brain Injury
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».