The relationship between neurobehavioural problems of severe traumatic brain injury (TBI), family functioning and the psychological well-being of the spouse/caregiver: path model analysis
Bibliographic record
Abstract
This study used a modern theory of stress as a framework to strengthen the understanding of the relationship between neurobehavioural problems of TBI, family functioning and psychological distress in spouse/caregivers. The research was an ex post facto design utilising a cross-sectional methodology. Path analysis was used to determine the structural effect of neurobehavioural problems on family functioning and psychological distress. Forty-seven female and 17 male spouse/caregivers of partners with severe TBI were recruited. Spouse/caregivers who reported partners with TBI as having high levels of behavioural and cognitive problems experienced high levels of unhealthy family functioning. High levels of unhealthy family functioning were related to high levels of distress in spouse/caregivers, as family functioning had a moderate influence on psychological distress. Furthermore, indirect effects of behavioural and cognitive problems operating through family functioning intensified the level of psychological distress experienced by spouse/caregivers. Additionally, spouse/caregivers who reported high levels of behavioural, communication and social problems in their partners also experienced high levels of psychological distress. This study was significant because the impact of TBI on the spouse/caregiver from a multidimensional perspective is an important and under-researched area in the brain injury and disability field.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".