A Qualitative Scoping Review of the Impacts of Economic Recessions on Mental Health: Implications for Practice and Policy
Bibliographic record
Abstract
In a follow-up to our 2021 scoping review of the quantitative literature on the impacts of economic recessions on mental health, this scoping review summarizes qualitative research to develop a descriptive understanding of the key factors that transmute the socioeconomic stressors of a recession into poorer mental health. The previous study identified 22 qualitative studies from 2008 to 2020, which were updated with search results from six databases for articles published between 2020 and 2021. After inclusion and exclusion criteria were applied to the total 335 identified studies, 13 articles were included. These were peer-reviewed, qualitative studies in developed economies, published from 2008 to 2021, and available online in English. Participants perceived that financial hardship and unemployment during recessions increased stress and led to feelings of shame, loss of structure and identity, and a perceived lack of control, which increased interpersonal conflict, social isolation, maladaptive coping, depression, self-harm, and suicidal behavior. Participants struggled with accessing health and social services and suggested reforms to improve the navigation and efficiency of services and to reduce the perceived harms of austerity measures. Providers should screen for mental distress and familiarize themselves with health and social resources in their community to help patients navigate these complex systems. Policy makers should be aware of the potential protective nature of unemployment safeguards and consider other low-cost measures to bolster mental health supports and informal social networks. Research in this area was limited. Further research would be beneficial given the impacts of the ongoing COVID-19 recession.
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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 | 0.099 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".