Health conditions, disability and economic inactivity in Northern Ireland. An administrative data study.
Bibliographic record
Abstract
Background Northern Ireland consistently experiences a higher rate of economic inactivity compared to other regions of the UK, currently 27% of the working age population compared to 22% in the UK. Historically, the major variance in explaining higher NI economic inactivity rates has been larger proportions of long-term sick/disabled. Only 34.7% of the NI population with a disability are employed compared to 77.9% of the non-disabled population. Aim The aims were to explore the relationship between chronic health status and the labour-market in NI, and how receipt of DLA is associated with economic inactivity. Methods This study links the 2011-NI-Census records, DLA-dataset, death registrations for the Census population, settlement-band data, the Land and Property Service capital-valuation of property, and the NI-Multiple-Deprivation Measure. The economically active population was defined as all individuals that were either employed or unemployed but looking for a job at the time of the 2011-Census. Results Men with mental-health conditions reporting a lot of limitation in day-to-day activities are almost 51 times more likely to be economically inactive compared to men with no health condition (ORadj=50.99, 95%CI:46.8,55.6). Learning/mental-health conditions are more likely to be associated with economic inactivity in both women and men compared to physical health conditions, such as long-term pain, mobility or breathing difficulties. Individuals in receipt of DLA are more than twice as likely to be economically inactive as their peers who are not in receipt of DLA. Conclusion Individuals with mental-health conditions reporting a lot of limitation in day-to-day activities have the lowest rates of participation in the labour-market. A lot of limitation in the day-to-day activities appears to be strongly associated with economic inactivity regardless of the health condition. DLA uptake is associated with considerably reduced likelihood of being economically active overall while the health conditions underlying DLA uptake are strong barriers to access the labour-market in NI.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".