The Impact of Economic Recessions on Depression, Anxiety, and Trauma-Related Disorders and Illness Outcomes—A Scoping Review
Bibliographic record
Abstract
In the wake of a global economic recession secondary to the COVID-19 pandemic, this scoping review seeks to summarize the current quantitative research on the impact of economic recessions on depression, anxiety, traumatic disorders, self-harm, and suicide. Seven research databases (PsycINFO, MEDLINE, Embase, Web of Science: Core Collection, National Library of Medicine PubMed, PubMed Central, and Google Scholar) were searched for keywords returning 3412 preliminary results published since 2008 in Organisation for Economic Coordination and Development (OECD)nations. These were screened by both authors for inclusion/exclusion criteria resulting in 127 included articles. Articles included were quantitative studies in OECD countries assessing select mental disorders (depression, anxiety, and trauma-/stress-related disorders) and illness outcomes (self-harm and suicide) during periods of economic recession. Articles were limited to publication from 2008 to 2020, available online in English, and utilizing outcome measures specific to the disorders and outcomes specified above. A significant relationship was found between periods of economic recession and increased depressive symptoms, self-harming behaviour, and suicide during and following periods of recession. Results suggest that existing models for mental health support and strategies for suicide prevention may be less effective than they are in non-recession times. It may be prudent to focus public education and medical treatments on raising awareness and access to supports for populations at higher risk, including those vulnerable to the impacts of job or income loss due to low socioeconomic status preceding the recession or high levels of financial strain, those supporting others financially, approaching retirement, and those in countries with limited social safety nets. Policy makers should be aware of the potential protective nature of unemployment safeguards and labour program investment in mitigating these negative impacts. Limited or inconclusive data were found on the relationship with traumatic disorders and symptoms of anxiety. In addition, research has focused primarily on the working-age adult population with limited data available on children, adolescents, and older adults, leaving room for further research in these areas.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".