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Record W4385961356 · doi:10.46743/2160-3715/2023.6198

Perceptions of Mental Health among Pakistani Women with Micro-Finance Loans: An Interpretive Descriptive Study

2022· article· en· W4385961356 on OpenAlexaff
Farhana Madhani, Catherine Tompkins, Susan M. Jack, Carolyn Byrne

Bibliographic record

VenueThe Qualitative Report · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcMaster UniversityBrock University
Fundersnot available
KeywordsMental healthPsychologyNonprobability samplingQualitative researchMedicineSociologyPsychiatryEnvironmental healthPopulationSocial science

Abstract

fetched live from OpenAlex

Mental health has gained significant recognition and importance as a crucial aspect of overall well-being. An individual's mental health is influenced by the intersection of individual, social, cultural, and systematic sources of stress and resilience. It is important to include subjective conceptualizations of mental health and well-being to develop culturally sensitive approaches to mental health promotion. This qualitative study aimed to understand how urban-dwelling women living in Pakistan who are micro-finance loan recipients conceptualize the meaning of mental health. Using interpretive description methodology, data were collected and analyzed through in-depth, semi-structured interviews conducted in Urdu with a purposeful sample of 32 women. An inductive approach to content analysis was employed to code and categorize the data. Participants conceptualized mental health as the presence of peace and the absence of tension. Chronic sources of tension included a lack of essential resources, safety, and security in their day-to-day living in Karachi, Pakistan. Implementing policies to address women’s basic needs, including access to education, would be a helpful first step towards mental health promotion for Pakistani women. Integrating concepts that reflect women’s understanding of mental health will also be a useful first step in developing culturally sensitive mental health assessment tools.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.079
GPT teacher head0.499
Teacher spread0.421 · 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
Published2022
Admission routes1
Has abstractyes

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