Characterising neuropsychological rehabilitation service users for service design
Bibliographic record
Abstract
Purpose – Needs of people following acquired brain injury vary over their life-course presenting challenges for community services, especially for those with “hidden” neuropsychological needs. Characterisation of subtypes of rehabilitation service user may help improve service design towards optimal targeting of resources. This paper aims to characterise a neuropsychologically complex group of service users. Design/methodology/approach – Preliminary data from 35 participants accepted for a holistic neuropsychological rehabilitation day programme were subject to cluster analysis using self-ratings of mood, executive function and brain injury symptomatology. Findings – Analysis identified three clusters significantly differentiated in terms of symptom severity (Cluster 1 least and Cluster 2 most severe), self-esteem (Clusters 2 and 3 low self-esteem) and mood (Cluster 2 more anxious and depressed). The three clusters were then compared on characteristics including age at injury, type of injury, chronicity of problems, presence of pre-injury problems and completion of rehabilitation. Cluster 2 were significantly younger at time of injury, and all had head injury. Research limitations/implications – Results suggest different subgroups of neuropsychological rehabilitation service user, highlighting the importance of early identification and provision of rehabilitation to prevent deterioration, especially for those injured when young. Implications for design of, and research into, community rehabilitation service design for those with “hidden disability” are considered. Originality/value – The paper findings suggests that innovative conceptual frameworks for understanding potentially complex longer term outcomes are required to enable development of tools for triaging and efficient allocation of community service resources.
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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 | 0.004 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".