Interventions Addressing Vision, Visual-perceptual Impairments Following Acquired Brain Injury: A Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND.: The existing literature on the effectiveness of interventions targeting vision, visual-perceptual impairments following acquired brain injury (ABI) is scarce and unlinked to occupational performance. PURPOSE.: To explore current occupational therapy practice in vision-rehabilitation among adults with ABI in Canada, and to determine the evidence-practice gaps. METHODS.: An online survey was made available through the Canadian Association of Occupational Therapists (CAOT) website, and disseminated to seven public healthcare institutions in Quebec. The survey collected respondent demographic information, and the types and frequency of treatments delivered. Descriptive statistics were conducted to determine interventions' frequency. Participant comments were collected and grouped into recurring themes. FINDINGS.: Over half (55%) of respondents regularly use evidence-based interventions when addressing visual acuity (VA) and visual field (VF) deficits, but only very few (3%) use it when dealing with oculomotor function and visual stress impairments. IMPLICATIONS.: Results gave a glimpse of interventions used and suggested the need for further research in vision rehabilitation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".