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Record W2105894409 · doi:10.1136/oemed-2011-100182

Business mergers and acquisitions and the risk of mental disorders: a population-based study

2012· article· en· W2105894409 on OpenAlexafffund
JianLi Wang, Scott B. Patten, Shawn R. Currie, Jitender Sareen, Norbert Schmitz

Bibliographic record

VenueOccupational and Environmental Medicine · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of ManitobaMcGill UniversityAlberta Health ServicesUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsAnxietyIncidence (geometry)Depression (economics)Mental healthPsychiatryMedicinePopulationAnxiety disorderAffect (linguistics)Major depressive episodePsychologyEnvironmental healthCognition

Abstract

fetched live from OpenAlex

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.

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

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.336
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2012
Admission routes2
Has abstractyes

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