Framework for describing community integration for people with acquired brain injury
Bibliographic record
Abstract
BACKGROUND/AIM: Community integration is the ultimate goal of rehabilitation of adults with acquired brain injury which has a high incidence in the Australian population. The literature shows a need for a more comprehensive framework for community integration. This study developed a descriptive community integration framework drawn from views of five stakeholder groups and was compared with four similar frameworks. METHODS: Thirty-seven experts in acquired brain injury, including practitioners, researchers, policy makers, people with acquired brain injury and family members participated. Using a Delphi method, an iterative process of surveys, interviews, and focus groups sought their views on community integration. Responses were analysed in three stages systematically to reduce a large quantity of raw data into a core set of descriptive themes. A final member checking process rated participants' agreement with the importance of each theme. RESULTS: Seven themes were identified and described: Relationships, Community Access, Acceptance, Occupation, Being at Home, Picking up Life Again, and Heightened Risks and Vulnerability. Themes were congruent with elements of the frameworks from the literature. CONCLUSIONS: Rich data came from the diverse stakeholders in the participant groups. Two unique themes reflected the importance of re-integration and recovering important aspects of previous lives, and identifying risks and vulnerabilities and providing safeguards. The framework reflected emphases that may be specific to acquired brain injury. It can be used as a basis for development of community integration programmes and outcome measures.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".