Using Photovoice to Explore Mental Health and Rural Homelessness in Beaufort County, NC, USA
Bibliographic record
Abstract
The objective of this study is to explore the intersection of mental health and homelessness in rural Beaufort County, North Carolina, USA, focusing on the social and emotional well-being (SEWB) of people experiencing homelessness (PEH) and their service providers. The research, which was conducted from 2018 to 2020 using the photovoice method, gathered data from 18 PEH and four service providers before and during the COVID-19 pandemic, uncovering the unique challenges faced by rural PEH. The study identifies key external (inadequate infrastructure, food insecurity, systemic marginalization) and internal barriers (stigma, invisibility, alienation) impacting SEWB. The article recommends policies that address these barriers promote inclusive planning, improve infrastructure, and deliver tailored services to foster social inclusion and resilience in rural communities. Keywords: rural homelessness, mental health, photovoice, social and emotional well-being_____________________________________________ Utiliser photovoix pour explorer la santé mentale et l'itinérance rurale dans le comté de Beaufort, Caroline du Nord, États-Unis RésuméL'objectif de cette étude est d'explorer l'intersection de la santé mentale et de l'itinérance dans le comté rural de Beaufort, en Caroline du Nord, aux États-Unis, en se concentrant sur le bien-être social et émotionnel (SEWB) des personnes sans abri (PEH) et de leurs prestataires de services. La recherche, menée de 2018 à 2020 à l’aide de la méthode photovoix, a rassemblé des données auprès de 18 PEH et de quatre prestataires de services avant et pendant la pandémie de COVID-19, révélant les défis uniques auxquels sont confrontés les PEH rurales. L’étude identifie les principaux obstacles externes (infrastructures inadéquates, insécurité alimentaire, marginalisation systémique) et internes (stigmatisation, invisibilité, aliénation) qui ont un impact sur le SEWB. L'article recommande que les politiques qui s'attaquent à ces obstacles favorisent une planification inclusive, améliorent les infrastructures et fournissent des services sur mesure pour favoriser l'inclusion sociale et la résilience dans les communautés rurales. Mots-clés : sans-abri en milieu rural, santé mentale, photovoix, bien-être social et émotionnel
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".