Systems analysis of community and health services for acquired brain injury in Ontario, Canada
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To conduct a systems analysis on community and health services for individuals with acquired brain injury (ABI) in the province of Ontario, Canada. RESEARCH DESIGN: This study employed a triangulation design. This design is used when there is a need to validate quantitative results with qualitative data, as is the case in the present study. METHODS AND PROCEDURES: Forty-two healthcare professionals and/or healthcare administrators from organizations across the province and across the continuum of care were surveyed. A 1-day focus group was also held to validate the study findings. MAIN OUTCOMES AND RESULTS: The main results of this study revealed: (1) a lack of services for children/adolescents; (2) service gaps for individuals with co-existing mental health conditions; (3) a lack of services related to employment; (4) changes in casemix, in terms of more individuals with co-morbid medical and mental health conditions (with many of the organizations reporting medical instability and severe behavioural disorders as exclusion criteria); and (5) a need for more organizations to track patient outcomes for evaluation and/or accountability purposes. CONCLUSIONS: Findings from this study will lead to improvement of current services but also improved planning of future services for individuals with ABI.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".