Coping With Stressors and General Resistance Resources Used by Individuals Experiencing Homelessness in Minneapolis Tent Camps
Bibliographic record
Abstract
Homelessness remains a significant public health issue across the United States, particularly in urban areas. Individuals become and remain homeless for multifaceted and complex reasons that are linked to well-being. The aim of this study was to understand the well-being of persons experiencing homelessness (PEH) and living in tent camps using Aaron Antonovsky’s salutogenic model of health (SMH). To address well-being, we conducted a basic qualitative study with thirty adults over age 18 who self-identified as homeless and living in tent camps within the city of Minneapolis. With a semi-structured interview guide that centered on the SMH, we analyzed data using Johnny Saldaña’s qualitative coding method. Sources of stress themes, including (1) “family trauma” (depression/trauma related to the death of a loved one and drugs, imprisonment, and abuse), (2) “mental health” (depression/trauma related to the death of a loved one, loneliness living in tent camps, substance use, mental illness), and (3) “change and threats” (constant fear of aggression, lack of stability of the tent camp, bad people causing problems, cliques in the camp). Themes of general resistance (GRRs) resources (coping with stress), or GRRs emerged, including (1) “systems knowledge,” (2) “coping strategies,” (3) “sense of community,” (4) “camp stability,” and (5) “human dignity” emerged during data collection and analysis. These findings can inform policy decisions related to increasing services to exit homelessness, funding for sustainable tent camps, and sweeps of tent camps in the city of Minneapolis and beyond.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".