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Record W4410159864 · doi:10.1186/s12982-025-00604-8

Addressing homelessness among people with mental illness in Ghana: suggestions from Nsawam residents

2025· article· en· W4410159864 on OpenAlexaff
George Ofosu Oti, Kwamina Abekah‐Carter, Joyce Dede Lartey, Margaret Amenuke-Edusei

Bibliographic record

VenueDiscover Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMental illnessPsychologyPsychiatryMental healthGerontologyMedicine

Abstract

fetched live from OpenAlex

Homelessness among people with mental illness is on the rise in many countries, including Ghana. To curb this phenomenon, appropriate measures must be taken by the relevant authorities. This study explored the perspectives of Nsawam community residents regarding how homelessness among people with mental illness could be addressed. Using the qualitative design, data were collected through semi-structured interviews with 21 participants, including traders, mental health nurses, and teachers, among others. Data collected from the interviews were subsequently transcribed and thematically analysed. Suggestions mentioned by participants were grouped under the following themes: (a) Public education; (b) getting people with mental illness off the streets; (c) establishing additional psychiatric facilities; and (d) involving family members. The tenets of the health belief model were also briefly applied to the findings. The study concluded that by considering community members’ perspectives and beliefs regarding severity, susceptibility, barriers, benefits, and cues to action, mandated institutions could design and implement effective interventions to promote support for homeless people with mental illness.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
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
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.062
GPT teacher head0.411
Teacher spread0.349 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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