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Record W4320501988 · doi:10.1051/shsconf/202315701022

The Effects of Poverty on Mental Health and Interventions

2023· article· en· W4320501988 on OpenAlexaff
Yihan Sun

Bibliographic record

VenueSHS Web of Conferences · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPsychological interventionMental healthPovertyAnxietyMental illnessPsychiatryPsychologyDepression (economics)Clinical psychologyGovernment (linguistics)MedicinePolitical science

Abstract

fetched live from OpenAlex

It is well established that the link between mental illness and poverty is adverse. Consistent research has shown that individuals with low income have regularly been shown to be linked to an increased incidence of mental illness. Mental health is a significant part of one’s life because it can influence emotions, thoughts, and actions. The purpose of this research is to examine how poverty affects mental health and offer alternative interventions. Three mental illnesses—depression, anxiety, and posttraumatic stress disorder (PTSD) are reviewed in particular, and practical solutions from the perspectives of family, education, and public health are suggested. This research concludes that parenting is a major factor that causes depression and anxiety among children, poor parents with depressed or anxious symptoms also increase the risk of the mental disorder for their children. The poor relatively easier to encounter trauma and have a greater impact after trauma. Additionally, financial assistance from the government and competent policy is essential for providing effective interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.000

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 head0.032
GPT teacher head0.335
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes1
Has abstractyes

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