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Record W4388446499 · doi:10.5206/ijoh.2023.3.15653

Coping With Stressors and General Resistance Resources Used by Individuals Experiencing Homelessness in Minneapolis Tent Camps

2023· article· en· W4388446499 on OpenAlexvenueno aff
Magdeline Aagard, Kasey Keeler

Bibliographic record

VenueInternational Journal on Homelessness · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersDivision of Graduate Education
KeywordsLonelinessMental healthImprisonmentCoping (psychology)StressorPsychologyQualitative researchDignityGerontologyPsychiatryMedicineCriminologySociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.377
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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