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Record W3087228052 · doi:10.1371/journal.pone.0239133

Social anxiety in young people: A prevalence study in seven countries

2020· article· en· W3087228052 on OpenAlexafffund
Philip Jefferies, Michael Ungar

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsDalhousie University
FundersDalhousie UniversityUnilever
KeywordsAnxietyYoung adultSocial anxietyMedicineEnvironmental healthDemographyPsychologyGerontologyPsychiatrySociology

Abstract

fetched live from OpenAlex

Social anxiety is a fast-growing phenomenon which is thought to disproportionately affect young people. In this study, we explore the prevalence of social anxiety around the world using a self-report survey of 6,825 individuals (male = 3,342, female = 3,428, other = 55), aged 16-29 years (M = 22.84, SD = 3.97), from seven countries selected for their cultural and economic diversity: Brazil, China, Indonesia, Russia, Thailand, US, and Vietnam. The respondents completed the Social Interaction Anxiety Scale (SIAS). The global prevalence of social anxiety was found to be significantly higher than previously reported, with more than 1 in 3 (36%) respondents meeting the threshold criteria for having Social Anxiety Disorder (SAD). Prevalence and severity of social anxiety symptoms did not differ between sexes but varied as a function of age, country, work status, level of education, and whether an individual lived in an urban or rural location. Additionally, 1 in 6 (18%) perceived themselves as not having social anxiety, yet still met or exceeded the threshold for SAD. The data indicate that social anxiety is a concern for young adults around the world, many of whom do not recognise the difficulties they may experience. A large number of young people may be experiencing substantial disruptions in functioning and well-being which may be ameliorable with appropriate education and intervention.

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)0.000
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 score0.078
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.283
Teacher spread0.234 · 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

Citations308
Published2020
Admission routes2
Has abstractyes

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