Psychosocial assessment in brain injury: An international social work survey
Bibliographic record
Abstract
To survey social workers in the field of traumatic brain injury (TBI)/acquired brain injury (ABI) about their practice in conducting psychosocial assessments. Design: A cross-sectional quality assurance study. A cross-sectional quality assurance study. Social workers from professional social work rehabilitation networks spanning Sweden, the United Kingdom, North America, and Asia Pacific regions. Purpose-designed survey comprising closed and open items, organized into six sections and administered electronically. The 76 respondents were mainly female (65/76, 85.5%) from nine countries (majority from Australia, United States, Canada). Two-thirds of respondents were employed in outpatient/ community settings (51/76, 67.1%), with the balance working in inpatient/rehabilitation hospital settings. Over 80% of respondents conducted psychosocial assessments, with the assessments informed by a systemic focus, situating the individual within their broader family and societal networks. The top five issues identified in inpatient/rehabilitation settings were housing related needs, informed consent for treatment, caregiver support, financial issues and navigating the treatment system. In contrast, the leading issues identified in community settings related to emotional regulation, treatment resistance and compliance issues, depression, and self-esteem. Social workers assessed a broad range of psychosocial issues spanning individual, family, and environmental contextual factors. Findings will contribute to future development of a psychosocial assessment framework.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".