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Record W4376131537 · doi:10.1037/ort0000681

Preference for online social interactions and support for violent radicalization among college and university students.

2023· article· en· W4376131537 on OpenAlexafffund
Diana Miconi, Tara Santavicca, Rochelle L. Frounfelker, Cécile Rousseau

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2023
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Heritage
KeywordsPsychologySocial supportRadicalizationPsychosocialSocializationPreferencePsycINFOSocial psychologyMediationAssociation (psychology)Clinical psychologyDevelopmental psychologyTerrorismMEDLINEPsychiatry

Abstract

fetched live from OpenAlex

= 7.45) responded to an online survey. We implemented multivariable mixed-effects regression models, stratified and mediation analyses. Greater preference for online social interactions was associated with stronger support for VR. Preference for online social interactions was a risk factor for VR, particularly at low levels of public self-esteem and social support as well as at high levels of importance attributed to one's group identity. Depressive symptoms partially mediated this association. Programs aimed to foster and value multiple identities and increase social support in educational settings are urgently needed to address the possible negative consequences of the online space on young people's mental health and support for violence. Prevention programs should address the provision of psychosocial support to students reporting depressive symptoms and help them build and maintain a supportive social network, as well as enhance inclusion at the societal level and across educational institutions. (PsycInfo Database Record (c) 2023 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)0.000
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 score0.031
Threshold uncertainty score0.361

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)0.0000.000

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 head0.034
GPT teacher head0.343
Teacher spread0.309 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

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