Business mergers and acquisitions and the risk of mental disorders: a population-based study
Bibliographic record
Abstract
OBJECTIVES: Mergers and acquisitions (M&A) activities are increasing and may negatively affect workers mental health. However, the impact of M&A on the risk of developing a mental disorder, rather than psychiatric symptoms, has not been investigated. The objectives of this study were to estimate and compare the 12-month incidence of depressive and anxiety disorders in workers who had and who had not experienced M&A in the last year. METHODS: Employees aged 25 and 64 years old were randomly selected from the community and were followed for 1 year (n=3280). Questions about their experience in M&A in the past 12 months were asked. WHO's Composite International Diagnostic Interview-Auto 2.1 was used to assess depressive and anxiety disorders. The 12-month prevalence and 1-year incidence of mental disorders were estimated and compared in relation to M&A. RESULTS: Participants who were exposed to M&A had a significant higher 1-year incidence of generalised anxiety disorder (GAD) (6.7%) than the unexposed (2.4%). They were not different in the incidence of major depressive disorder. The exposed participants were 2.8 times more likely to have had a GAD than others and were about 2.4 times more likely to have developed any anxiety disorders over 1 year. CONCLUSIONS: M&A may lead to increased risk of GAD, which may, in return, evolve into major depression. Governments, employers and health professionals should be aware of this and work out plans to reduce the negative health outcomes of M&A.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".