MétaCan
Menu
Back to cohort
Record W3160769426 · doi:10.3390/ijerph18105250

Housing, Living Arrangements and Mental Health of Young Adults in Independent Living

2021· article· en· W3160769426 on OpenAlexaff
Bo Kyong Seo, Gum‐Ryeong Park

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMental healthYoung adultContext (archaeology)Psychological interventionPsychologyAnxietyWelfareGerontologyMedicinePsychiatryDevelopmental psychologyGeography

Abstract

fetched live from OpenAlex

Young adults are prone to psychological stress and anxiety induced by major transitions to adulthood. While employment has predominated in previous research on the social determinants of young people’s mental health, this study examines the association between young people’s housing problems and mental health in the context of an unaffordable housing market. Using the Survey on the Living Conditions and Welfare Needs of Youths (n = 1308) in Korea, the study found that perceived poor housing quality and material hardship are negatively associated with the mental health of young adults living independently. Specifically, while poor housing quality and material hardship induced by housing cost burden were negatively associated with single-person households’ mental health, only poor housing quality was associated with non-single-person households’ mental health. This study is one of the few studies examining the linkage between housing problems and mental health of young adults and informs the interventions aimed at promoting the psychological well-being of young adults in the transition from parents’ homes to independent living.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.066
GPT teacher head0.389
Teacher spread0.323 · 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

Citations28
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicUrban, Neighborhood, and Segregation StudiesFrench-language works237,207