Homelessness and Mental Illness: Using Participatory Action Research to Inform Mental Health Counseling
Bibliographic record
Abstract
Homelessness and mental illness frequently occur together. Individuals who have a mental illness and are homeless may experience a range of challenges and are often underserved by behavioral health professionals. This study used Participatory Action Research to form a working group (n=6), which met at a homeless shelter located in a small city in the United States (U.S) mid-Atlantic region, to generate insights and responses to the challenges they had encountered. The authors posed several research questions in the context of lived experiences of individuals who experience homelessness and mental illness. The researchers used Interpretive Phenomenological Analysis to interpret the data in light of these questions. We generated four key themes: the trauma of homelessness and mental illness, the power of personal connection, personal agency, and achievement, and meaning through action. For mental health counselors, suggestions include incorporating a trauma-informed framework, minimal turnover in counselor coverage, and a client-centered approach.
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 | 0.107 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".