Spatial Neglect: An Exploration of Clinical Assessment Behaviour in Stroke Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: There is a large gap between evidence-based recommendations for spatial neglect assessment and clinical practice in stroke rehabilitation. We aimed to describe factors that may contribute to this gap, clinician perceptions of an ideal assessment tool, and potential implementation strategies to change clinical practice in this area. DESIGN: Qualitative focus group investigation. Focus group questions were mapped to the Theoretical Domains Framework and asked participants to describe their experiences and perceptions of spatial neglect assessment. SETTING: Online stroke rehabilitation educational bootcamp. PARTICIPANTS: A sample of 23 occupational therapists, three physiotherapists, and one orthoptist that attended the bootcamp. INTERVENTION: Prior to their focus group, participants watched an hour-long educational session about spatial neglect. MAIN MEASURES: A deductive analysis with the Theoretical Domains Framework was used to describe perceived determinants of clinical spatial neglect assessment. An inductive thematic analysis was used to describe perceptions of an ideal assessment tool and practice-change strategies in this area. RESULTS: Participants reported that their choice of spatial neglect assessment was influenced by a belief that it would positively impact the function of people with stroke. However, a lack of knowledge about spatial neglect assessment appeared to drive low clinical use of standardised functional assessments. Participants recommended open-source online education involving a multidisciplinary team, with live-skill practice for the implementation of spatial neglect assessment tools. CONCLUSIONS: Our results suggest that clinicians prefer functional assessments of spatial neglect, but multiple factors such as knowledge, training, and policy change are required to enable their translation to clinical practice.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".