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Record W3204023256 · doi:10.1037/ort0000598

The mediating role of social climate in the association of youth and residential service characteristics and quality of life.

2022· article· en· W3204023256 on OpenAlexaff
Jonathan D. Leipoldt, Annemiek Harder, Nanna S. Kayed, Erik J. Knorth, Tormod Rimehaug

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsOntario Centre of Excellence for Child and Youth Mental Health
FundersAmerican Psychological Association
KeywordsNorwegianLatent class modelAssociation (psychology)Quality of life (healthcare)PsychologyGerontologyStructural equation modelingPopulationClinical psychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Previous research has shown that social climate (SC) is important for the daily life of youths living in therapeutic residential youth care (TRC). However, little is known on how SC can promote a positive quality of life (QoL) for the heterogeneous TRC population. This study, therefore, investigates how TRC and youth characteristics are associated with SC and QoL. We employed a combination of person-centered and variable-centered approaches in a cross-sectional design using a sample of 400 Norwegian youths. We used previously established TRC and youth classes in a structural equation model, where these classes were regressed on latent SC and QoL. Both direct and indirect effects were analyzed. All youth classes were associated with SC and QoL, such that youth with family problems, incidental problems, and the migrant background class scored higher on SC and QoL compared to the severe problems class. In addition, SC mediated the association of the incidental problems and migrant background classes on QoL. TRC staff should acknowledge that a positive SC can strengthen the QoL of youths with severe problems. Future research should longitudinally investigate these associations to establish long-term effects on QoL during stay in TRC. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.361
Teacher spread0.337 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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