ArticleBioMedicine2021
Predicting mental health of prisoners by artificial neural network.
Article in BioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Resilience and associated factors within the mental health profile of incarcerated adults in Portugal: a cross-sectional study.BMC psychiatry · 2026Article
- Encryption chain based on measurement result and its applications on semi-quantum key distribution protocol.Scientific reports · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMaintaining and improving prisoners' health and their rehabilitation can be effective steps towards eliminating health inequalities and approaching the UN's Sustainable Development Goals. Accordingly, identifying protective factors and health barriers of this vulnerable group and changing the prison into an environment that can deliver health interventions tailored to the needs of inmates can provide the basis for attaining justice in health. PURPOSE: This study builds on an artificial neural network model to determine the effect of demographic, psychological, criminological, and physical activity factors on prisoners' general health.
methodsThe study detected the patterns between variables using a neural network with nine inputs and one output. To determine the neural network with the minimum sum of squared errors, we evaluated the performance of all neural networks using varying algorithms and numbers of neurons in the hidden layer. For this purpose, the analysis of the data of 149 prisoners aged between 16 and 61 years was performed using SPSS-22 software.
resultsThe optimal neural network model was useful in predicting prisoners' general health. In this model, the variables of occupation, life expectancy, age, and hope of acceptance were identified as the first most significant factors with 19.25, 17.45, 15.98, and 15.16 percentages, respectively, whereas the cause of incarceration, education level, type of drug misuse, and physical activity were the second most important factors with 8.82, 8.38, 7.91, and 7.04 percentages, respectively.
conclusionExperiencing psychosocial pressures related to incarceration is more severe for the marginalized and disadvantaged individuals, persons in very young or old age ranges, and those with no history of incarceration, which can increase the likelihood of impaired health for these inmates. Consideration of the prisoners' needs in proportion to their characteristics and provision of emotional and spiritual support of the inmates, especially in the early stages of incarceration, can help shape an effective adjustment process and select appropriate and efficient strategies.
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