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Record W3121635444 · doi:10.1111/cdev.13483

Investigating the Interrelations Between Systems of Support in 13- to 18-Year-Old Adolescents: A Network Analysis of Resilience Promoting Systems in a High and Middle-Income Country

2021· article· en· W3121635444 on OpenAlexaffabout
Jan Höltge, Linda Theron, Angelique van Rensburg, Richard G. Cowden, Kaymarlin Govender, Michael Ungar

Bibliographic record

VenueChild Development · 2021
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsDalhousie University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsInterconnectivityPsychological resiliencePsychologyContext (archaeology)Resilience (materials science)Developmental psychologyPersonal networkResource (disambiguation)Low and middle income countriesSocial supportSocial psychologyGeographyComputer scienceDeveloping countryEconomic growth

Abstract

fetched live from OpenAlex

Adolescents' ability to function well under adversity relies on a network of interrelated support systems. This study investigated how consecutive age groups differ in the interactions between their support systems. A secondary data analysis of cross-sectional studies that assessed individual, caregiver, and contextual resources using the Child and Youth Resilience Measure (Ungar & Liebenberg, 2005) in 13- to 18-year-olds in Canada (N = 2,311) and South Africa (N = 3,039) was conducted applying network analysis. Individual and contextual systems generally showed the highest interconnectivity. While the interconnectivity between the individual and caregiver system declined in the Canadian sample, a u-shaped pattern was found for South Africa. The findings give first insights into cross-cultural and context-dependent patterns of interconnectivity between fundamental resource systems during adolescence.

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)0.002
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 score0.006
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.054
GPT teacher head0.350
Teacher spread0.297 · 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

Citations17
Published2021
Admission routes2
Has abstractyes

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