Poverty and Serious Mental Illness: Toward Action on a Seemingly Intractable Problem
Bibliographic record
Abstract
This paper examines the issue of poverty among people with serious mental illness (SMI), positioning it as a key issue to be confronted by community mental health systems and practitioners. The paper reviews three perspectives on poverty, considering how each sheds light on poverty among people with SMI, and their implications for action: (a) monetary resources, (b) basic needs, and (c) capabilities. The paper argues that community mental health programs and systems are currently unable to address poverty as they are overly focused on individual-level interventions that, on their own, cannot raise people out of poverty. The paper calls for a social justice value, informed by the concept of citizenship, as a necessary complement to the recovery concept that has informed community mental health practice for almost 25 years. Finally, the paper argues that community psychologists, with their concepts, methods, and values, are well positioned to contribute to this important issue. However, it also contends that addressing poverty requires collaboration from community psychologists with researchers and practitioners from other fields and domains of expertise to begin to make progress.
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
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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.063 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.019 | 0.032 |
| 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".