Referral patterns for post-acute inpatient brain injury rehabilitation in England: Who are the minorities?
Bibliographic record
Abstract
Belonging to a minority ethnic background affects access to health and social care services. The sparse research available has been one of the factors limiting our understanding of this problem. Within the UK, there appear to be no published data around referral patterns of ethnic minority groups for inpatient neurorehabilitation following a brain injury. This study used Freedom of Information (FOI) requests to obtain data around rehabilitation referral patterns across England. Of the 42 Integrated Care Boards (ICB) approached, 35 responded. Data on ethnicity of the population served was provided in 30 (71%) cases. Information on referrals to inpatient neurorehabilitation was provided by 23 (66%) of the respondents, but a breakdown of the ethnicity of the referrals was only available for seven ICB's (30%), and the largest category of ethnicity on record was "unknown", or "undeclared." There are barriers to the capture and reporting of ethnic information, particularly for minority groups, but uncertainty as to whether this stems from patients' choice or reluctance in disclosing this, or from the minimum data capture requirements within services, or both. The absence of these data prevents the development of improvement strategies to audit and mitigate drivers of inequality.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".