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Record W4387570497 · doi:10.56508/mhgcj.v6i1.176

Exploring the Feasibility of Integrating Mental Health into a Family Planning Program in low-resource settings

2023· article· en· W4387570497 on OpenAlexaff
Zahra Sarmad, Rida Z. Shah, Fareeha Javaid, Hasha Siddiqui, M. S. Qazi, Aneeta Pasha

Bibliographic record

VenueMental Health Global Challenges Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMental healthResource (disambiguation)Transformative learningNursingInterpersonal communicationPsychologyPublic relationsMedical educationKnowledge managementMedicinePolitical scienceComputer sciencePedagogyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Introduction: Mental health challenges remain a pressing issue, underscored by the glaring gap between the elevated demand and the scarce resources. Research has highlighted the effectiveness of integrating mental health services with primary care services, particularly in low-resource settings. Purpose: The objective of this research was to evaluate the perceived implications and feasibility of integrating basic mental health services into an existing community-based family planning initiative in Pakistan. By adopting a community-driven and co-produced methodology, our study not only ensured a deeper resonance with local needs but also paved the way for a sustainable and transformative uptake of mental health services in low-resource settings. This co-produced strategy, anchored in mutual collaboration and shared expertise with the community, promises a more holistic, enduring, and adaptive integration of essential health services within community frameworks.Methodology: This study utilized a qualitative research approach to obtain a comprehensive understanding of the program's feasibility and potential for expansion. Interview tools and guides, tailored to the regional language, were developed by the Research Associate to gather insights from the lady health workers involved in delivering the intervention, as well as from the clients. Overall, our team conducted 24 interviews, of which 9 were with the lady health workers and 15 with clients. The interviews were facilitated by the Research Associate and a Psychologist.Results: Utilizing the socio-ecological model, we thematically analyzed factors at individual, interpersonal, and community levels that support or hinder the integration of mental health services with existing community-based programmes. We also examined the intervention's impact on its users and the healthcare providers.Our analysis underscores the significant potential of integrating mental health services into existing community-based health programmes, such as family planning, in low-resource settings. Predominant themes highlighted women's willingness to use these services, influenced by strong relationships and trust in the lady health workers, ease of access to services, and community support. Identified barriers to integration included prevailing poverty, a preference for direct financial incentives in addition to counseling, confidentiality concerns in tight-knit communities, and the lingering stigma surrounding mental health.Conclusion: Our findings highlight the value of community collaboration in healthcare, particularly in low-resource settings. The co-production approach blends professional guidance with local insights, fostering community ownership and enhancing program sustainability. As the first to merge mental health with family planning in Pakistan, our research suggests that future health initiatives can greatly benefit from community-driven methods, leading to more sustainable and transformative health outcomes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.148
GPT teacher head0.411
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes1
Has abstractyes

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