Experiences of Mental Distress by Individuals during an Economic Downturn: The Story of an Urban City
Bibliographic record
Abstract
Introduction A variety of social conditions or statuses are involved in determination of risk for symptoms of mental distress. While everyone experiences some amount of stress, economic hardship and insecurity are stressors that can be expected to have immediate and powerful negative consequences for mental health. Frequency of mental distress are related to low income and economic insecurity, but in complex ways. Since people living on low incomes, who in American society are also disproportionately of minority racial or ethnic status, experience higher levels of stress in their daily lives, research has generally indicated that the poor and members of minority groups are more likely to exhibit higher than average frequencies of mental distress (Muntaner, Eaton, and Diala, 2000; Meyer, 2003). Research signals other vulnerable groups as well, especially the young and sexual minorities; while, conversely, there is ample evidence that as age and income increase, mental health, as measured by frequency and severity of mental health symptoms, improves (Harris et al, 2006). Access to Mental Healthcare Access to mental healthcare, while aided by mental healthcare parity laws passed in 1996 and updated in 2007 and 2008 (HHS and Pear, 2008), remains problematic, especially for those who are uninsured or underinsured. In addition to stigmas attached to seeking mental healthcare, economic concerns often factor into decisions about whether or not to seek mental healthcare, as well as an individual's ability to continue mental health treatment. A national survey based on data from 2001 to 2003 found that while there is a largely unmet need for mental healthcare in the U.S., the burden is greatest for traditionally underserved groups (Wang, et al, 2005). One study comparing visits to mental health care providers in the United States and Canada during the 1990s found that overall, 8.8% of Americans report one or more annual visits to the health sector for a mental health problem, compared to 6.9% of Canadians in Ontario. Americans with the highest incomes and no previous history of severe mental illness are much more likely to receive services than their Canadian counterparts. By contrast, Americans with the lowest incomes and high morbidity are much less likely to receive services for mental health problems than are a similar group of Canadians (Katz, S.J. et al 1997). These realities, in combination with the prevalence of 'sub-threshold' mental health conditions, such as minor depression, force many individuals to experience disparate impairments to daily life, without the relief of appropriate, accessible mental healthcare that is within their legally protected rights (Pincus 1999). The Economic Downturn In the fall of 2008, statements regarding an economic downturn began to appear in the mainstream media. In September of 2008, two major mortgage companies were taken over by the federal government, followed by the collapse of a number of major banks (Washington Post, 2008). Lay-offs skyrocketed and the New York State unemployment rate rose to 8.0% by April 2009, from 5.1% a year previously (NY State Department of Labor, 2009). Concurrently, sub-prime mortgage securitization schemes began to face financial ruin, and many began to fear losing their homes, their savings, and, perhaps most significantly for stressors that impair mental health, their medical insurance. From anecdotal accounts that began to appear in the media, this increasingly unstable economic environment appeared to cause a great deal of mental distress among New Yorkers. This paper offers empirical evidence that confirms these accounts and allows us to shed more light on which demographic groups in New York City, where the financial crisis hit so uniquely, are most affected. SES, Stress, and Health Socioeconomic status (SES) has strong predictive value for many disparate health outcomes, but is less well-described as a predictor of mental health (Adler and Ostrove, 1999; Roy-Byrne 2009). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".