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Record W4376141924 · doi:10.1080/01488376.2023.2198279

Risk, Resilience, and Chinese Youth’s Psychosocial Adjustment: The Role of Social Services

2023· article· en· W4376141924 on OpenAlexaff
Xiaoping Xiang, Juan Wang, Guoxiu Tian, Michael Ungar, Lili Han

Bibliographic record

VenueJournal of Social Service Research · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychosocialProsocial behaviorPsychological resiliencePsychologyChinaSocial supportClinical psychologyDevelopmental psychologySocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Few empirical studies have examined the impact of the universal social services provided to ordinary youth on their resilience and psychosocial adjustment, especially youth from developing countries like China. Based on a sample of 857 high school students between the ages of 13–19 from Beijing, this article examines the pattern of social services provided to Chinese youth and the impact of these services on their resilience and positive or negative psychosocial adjustment (depression, delinquency, prosocial behavior). The results reveal that: (a) Chinese youth generally make very limited use of services but they report moderate satisfaction; (b) both frequency and satisfaction of service use significantly predicts resilience, and resilience significantly predicts lower depression and higher prosocial behavior; (c) resilience fully mediates the relationships between social service frequency/satisfaction and depression/prosocial behavior. The findings give support to the public health model of social services and reveal that building resilience is one mechanism that universal service contributes to youths’ psychosocial adjustment. Future research may further investigate the relationships between social services, resilience and psychosocial adjustment in other geographical areas and time periods of China, and comparative studies between China and other countries may also be useful.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.461
Teacher spread0.420 · 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 designQualitative
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

Citations3
Published2023
Admission routes1
Has abstractyes

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