Evidence map›Paper›PMID 38161726›Full record

ArticleFrontiers in psychiatry2023

Multilayer perceptron modeling for social dysfunction prediction based on general health factors in an Iranian women sample.

Sajjad Bagheri, Sarvenaz Taridashti, Hojjatollah Farahani, Peter Watson, Elham Rezvani

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. When Health Behavior Meets Technology: Artificial Neural Network-Based Prediction of Breast Self-Examination Intentions Among Filipino Female College Students.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sajjad BagheriClinical Psychology, Department of Psychology, Hakim-Toos Institute of Higher Education, Mashhad, Iran.
Sarvenaz TaridashtiIndustrial and Organizational Psychology, Department of Psychology, Montclair State University, Montclair, NJ, United States.
Hojjatollah FarahaniDepartment of Psychology, Tarbiat Modares University, Tehran, Iran.
Peter WatsonMRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
Elham RezvaniClinical Psychology, Department of Psychology, Hakim-Toos Institute of Higher Education, Mashhad, Iran.

Funding

Medical Research Council MC_U105579212Medical Research Council MC_UU_00005/14
6 · The paper itself

Abstract

In the year 2022, this research conducted an in-person study involving 780 single or widowed women, aged between 20 and 70, falling within the bottom three economic deciles and possessing varying levels of education. All participants held educational qualifications below a high school diploma and were beneficiaries of charitable financial support in Khorasan province, Iran. The study aimed to investigate the predictive factors of social dysfunction in this specific demographic. Data collection spanned a 12-month period throughout 2022, with participants completing the GHQ-28 questionnaire during their visits to the charity office. Clinical in-person interviews were also conducted to gather comprehensive data. Data analysis was carried out using IBM SPSS version 27. The research employed a Multilayer Perceptron (MLP) neural network model, considering an extensive set of input factors and covariates. These factors included cognitive functioning, anxiety, depression, age, and education levels. The MLP model exhibited robust performance, achieving high overall accuracy and sensitivity in identifying cases of high social dysfunction. The findings emphasized the significance of cognitive functioning, anxiety, and depression as pivotal predictors of social dysfunction within this specific demographic, while education and age displayed relatively lower importance. The normalized importance scores provided a relative measure of each covariate's impact on the model's predictions. These results furnish valuable insights for the development of targeted interventions and evidence-based policies aimed at addressing social dysfunction and promoting societal well-being among economically disadvantaged, single or widowed women. Notably, the research underscores the potential of MLP modeling in social science research and suggests avenues for further research and refinement to enhance the model's predictive accuracy, particularly for cases of low social dysfunction.

Indexed as

anxietyartificial neural networkdepressionlow-income womenmultilayer perceptronsocial dysfunction

Identifiers

PMID38161726
PMCPMC10756140

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.