Clinicians’ perceptions of factors contributing to complexity and intensity of care of outpatients with traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: This study investigated clinicians' perceptions on factors linked to patient complexity in traumatic brain injury (TBI) outpatient rehabilitation. METHOD: Twelve clinicians from various disciplines, working in TBI outpatient programmes from three rehabilitation institutions in Montreal, Quebec, were recruited using convenience and snowball sampling. Data was collected through focus groups and individual interviews and thematic analysis was used to identify themes. MAIN OUTCOMES AND RESULTS: Participants identified complexity factors falling under the following themes: sequelae of TBI (cognitive/behavioural/psychological impacts), personal factors (personality traits, pre-medical state, lifestyle and age), patients' environment (architectural, social, language, cultural and financial) and therapeutic relationship (mismatch, misunderstanding and personality clashes). Clinicians also reported facilitators to optimal treatment delivery such as quality of services and working in an interdisciplinary team. Limited time, training and resources were identified as barriers to treatment. CONCLUSION: A substantial proportion of patients in outpatient TBI programmes seem to follow an atypical evolution and exhibit added complexity. In order to optimize quality of care, clinicians recommended increased community awareness about TBI, increased resources for rehabilitation clinicians and specialized services post-discharge. These findings are insightful for stakeholders; providing a basis for discussions on policy changes that can better meet this population's needs.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".