Are Occupational Therapists Losing Sight of Hemianopia?
Bibliographic record
Abstract
The purpose of this study was to determine the knowledge base surrounding hemianopia and to collate the rehabilitation principles offered by members of the National Association of Neurological Occupational Therapists (NANOT). A questionnaire was sent to 250 randomly selected members of NANOT. The completed questionnaires (n = 120) represented approximately a quarter of the total number of NANOT members at the time of the study. The mean post-registration time of the respondents was 11 years (SD 0.6). All United Kingdom geographical areas apart from Northern Ireland and the Isle of Man were represented. A wide range of clinical areas was also represented. The results showed that 92% of the respondents provided an accurate definition of hemianopia as the loss of half of the visual field. However, 48.3% reported that they were not testing every individual with a stroke for hemianopia. A third of the respondents stated that 80–100% of individuals with hemianopia always needed occupational therapy to compensate. The respondents also rated their understanding of eight neurovisual terms and, out of a total possible score of 80 (full understanding of terms), the mean score was 41 (SD 2.9). The occupational therapist's role in the assessment/rehabilitation of hemianopia emerged in four categories: education, compensation, assessment of effects and diagnosis. Even if individuals were made aware of their hemianopia, 62% of the respondents reported that there were resulting problems in the individual's engagement in occupation (aspects of self-care, productivity and leisure). These results are discussed in the context of the available literature and conclusions are drawn. A recommendation is made to improve the awareness and rehabilitation of individuals with hemianopia by occupational therapists.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".