Development of an online awareness assessment for adults with neurological conditions: a Delphi study
Bibliographic record
Abstract
PURPOSE: Online awareness, or the ability to detect errors and monitor performance during activities, is a component of functional cognition essential for safety and independence within everyday activities. There is a lack of assessment methods that target online awareness for individuals with neurological conditions. This study aimed to make recommendations for a clinically useful method for assessing online awareness in rehabilitation. MATERIALS AND METHODS: The methods included a conceptual analysis to define online awareness and identify existing assessment approaches; three rounds of Delphi surveys involving an international expert panel to develop an online awareness assessment; and pilot testing of the assessment with consumers. RESULTS: The conceptual analysis generated an online awareness definition framework that included four key elements: appraisal, anticipation/prediction, monitoring, and self-evaluation. This framework guided survey questions posed to the expert panel. The Delphi process supported development of the 'Online Awareness Behaviours Scale', an assessment that can be used in clinical practice and research. Consumer feedback indicated high levels of usability and acceptability of the assessment. CONCLUSIONS: The Online Awareness Behaviours Scale is a new, clinically acceptable tool for rehabilitation clinicians that assesses online awareness of individuals with neurological conditions.
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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 | 0.068 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".