Bibliographic record
Abstract
Purpose – The purpose of this paper is to use longitudinal Canadian data from the National Population Health Survey (1994-2006) to examine the impact of provincial unemployment rate on mental health as measured by the short form depression scale. Design/methodology/approach – To control for the unobserved individual specific factors, the study utilized individual-specific fixed-effects model. Findings – The study found that, for the overall model, provincial unemployment rate has a significant positive impact on depression. The study further examined the impact of unemployment rate on depression for a number of sub-groups based on gender, age, marital status, and education. The results suggest that the impacts of unemployment rate on depression are heterogeneous across different sub-groups. Practical implications – The results of this study have important policy implications. Previous studies suggest that mental stress may lead to risky health behaviours such excessive drinking, substance use, and smoking. These risky health behaviours may have long term health consequences in terms of chronic conditions such as heart disease, cancer, etc. Thus policy makers may consider taking appropriate steps to provide mental health support during the period of recession. Such support may also be helpful for the unemployed individuals who are too depressed to search for job. Originality/value – Previous studies on this issue may suffer from potential bias since they omitted unobserved individual specific factors from the estimating equations. This paper has taken the opportunity of utilizing longitudinal Canadian Population Health Survey and adopts an individual specific fixed effects method to estimate the effects of macroeconomic conditions on mental health. All of the studies reviewed here used data from the USA. So far no study has examined the impact of unemployment rate on mental health using Canadian data. It is interesting to conduct a study using Canadian data since there are important differences between Canada and the USA with respect to labour market policies and health care systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".