Returning to âstatus quoâ? Multiple perspectives on community reintegration and people with brain injuries
Bibliographic record
Abstract
Brain injuries (BI) are the leading cause of death and disability among people under the age of 45 (Ontario Brain Injury Association, 2004). With improved survival rates, more individuals each year return to the community with impairments and disabilities caused by their injury (Smith, Magill–Evans, and Brintnell, 1998). Adjusting to these impairments may affect the individual’s subjective well being; therefore, attention to community reintegration by researchers, policy developers, and health care providers is important. Using qualitative research methods and systems theory as the theoretical framework, the purpose of the study was to examine community reintegration from the perspectives of three key groups: individuals with BI, community based agencies, and primary care physicians regarding the meaning attributed to “successful reintegration”, as well as the key characteristics and barriers experienced during reintegration. “Successful” reintegration appears to be an individually derived concept. Participants consistently identified the need for information about the process of community reintegration, and resources available both during rehabilitation and after discharge from the hospital as being both a key aspect of community reintegration, as well as a barrier experienced during the return to community.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".