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Record W4417178309 · doi:10.1016/j.ssmmh.2025.100575

Beyond belief: understanding contexts for help-seeking for severe mental illness in urban slum communities in Dhaka, Bangladesh and Ibadan, Nigeria

2025· article· en· W4417178309 on OpenAlexaff
Ursula M. Read, Bulbul Siddiqi, Kafayat Aminu, Adeola Afolayan, Aman Haque, Ashrafuzzaman Khan, Nadia Alam, Srividya N. Iyer, Obafemi Jegede, Akinyinka Omigbodun, Olayinka Omigbodun, Tanjir Rashid Soron, Sagar Jilka, S. Daniel Sundar Singh

Bibliographic record

VenueSSM - Mental Health · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteDouglas College
FundersNational Institute for Health and Care Research
KeywordsSlumMental illnessMental healthQualitative researchFocus groupQuality (philosophy)FaithQuality of life (healthcare)Explanatory model

Abstract

fetched live from OpenAlex

Research on help-seeking for serious mental illness (SMI) has often focused on the influence of cultural beliefs and explanatory models however a number of factors also influence decisions around treatment including availability, cost and quality of care. This study utilises an ecological framework to explore influences on help-seeking for serious mental illness in deprived urban slum communities in Ibadan, Nigeria and Dhaka, Bangladesh. Interviews and observation were conducted with family caregivers and people with lived experience of SMI. Although in both settings some participants speculated on the role of spiritual agents they also expressed uncertainty around the causes of SMI. Families commonly sought help from both biomedical practitioners and traditional and faith healers Help-seeking was embedded within complex ecologies of care, informed by considerations of quality, efficacy and cost, as much as beliefs. The complexity of influences on help-seeking and dissatisfaction with health services as well as healers suggests that a contextually informed, multi-component approach is needed which addresses health system weaknesses and affordability as well as accessibility.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.379
Teacher spread0.332 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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