Poor mental health and uptake of disability benefits
Bibliographic record
Abstract
Background with rationale Disability related social security benefits were introduced to help off-set the additional costs incurred by people with disability and to help them better integrate into society. However, there are concerns that not everyone with a disability is receiving the benefits to which they are entitled. Aim To undertake a record-linkage study to compare levels of benefit uptake Disability Living Allowance (DLA) according to disability type and severity as reported in the Northern Ireland 2011 Census. Data/methods The linked data sources were the 2011 census records and all DLA records for calendar year 2011. The main comparison was of DLA receipt amongst those reporting chronic mobility difficulties (MD) and those with chronic poor mental health (PMH) (both yes/no) with the addition of limitation of daily activities (none, a little, a lot), which was used as a proxy for severity and DLA eligibility. Results Most people with disability had multiple disabilities but here the analysis is primarily amongst people with solitary conditions to avoid potential confounding. Approximately 20,000 people in the population reported either only MD or only PMH, though 68% of MD had their daily activities limited a lot compared to 55% of those with PMH. Overall, 71.8% of people with MD and daily activities limited a lot were on DLA compared to only 57.5% of similarly affected people with PMH the relative odds ratio being (OR 0.53 95%CI 0.50, 0.56). Conclusion It appears that people with poor mental health are less likely to receive disability benefits compared to those with mobility problems. This may be because they are less able to prove their eligibility for these benefits under the current assessment processes.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".