Changing labour market conditions during the ‘great recession’ and mental health in Scotland 2007–2011: an example using the Scottish Longitudinal Study and data for local areas in Scotland
Bibliographic record
Abstract
This paper reports research exploring how trends in local labour market conditions during the period 2007-2011 (early stages of the 'great recession') relate to reported mental illness for individuals. It contributes to research on spatio-temporal variation in the wider determinants of health, exploring how the lifecourse of places relates to socio-geographical inequalities in health outcomes for individuals. This study also contributes to the renewed research focus on the links between labour market trends and population health, prompted by the recent global economic recession. We report research using the Scottish Longitudinal Study (SLS), a 5.3% representative sample of the Scottish population, derived from census data (https://sls.lscs.ac.uk/). In Scotland, (2011) census data include self-reported mental health. SLS data were combined with non-disclosive information from other sources, including spatio-temporal trends in labour market conditions (calculated using trajectory modelling) in the 32 local authority areas in Scotland. We show that, for groups of local authorities in Scotland over the period 2007-2011, trends in employment varied. These geographically variable trends in employment rates were associated with inequalities in self-reported mental health across the country, after controlling for a number of other individual and neighbourhood risk factors. For residents of regions that had experienced relatively high and stable levels of employment the odds ratio for reporting a mental illness was significantly lower than for the 'reference group', living in areas with persistently low employment rates. In areas where employment declined markedly from higher levels, the odds ratio was similar to the reference group. The findings emphasise how changes in local economic conditions may influence people's health and wellbeing independently of their own employment status. We conclude that, during the recent recession, the economic life course of places across Scotland has been associated with individual mental health outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".