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Record W4399771510 · doi:10.2196/58739

Problematic Social Media Use Among Italian Midadolescents: Protocol and Rationale of the SMART Project

2024· article· en· W4399771510 on OpenAlexvenueno aff
Valeria Donisi, Laura Salerno, Elisa Delvecchio, Agostino Brugnera

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaPsychological interventionPsychologyProtocol (science)PopulationQualitative researchDistressApplied psychologyInternet privacyClinical psychologySocial psychologyDevelopmental psychologyMedicineComputer scienceEnvironmental healthAlternative medicineWorld Wide WebPsychiatrySociology

Abstract

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BACKGROUND: Social media (SM) use constitutes a large portion of midadolescents' daily lives as a way of peer interaction. A significant percentage of adolescents experience intense or problematic social media use (PSMU), an etiologically complex behavior potentially associated with psychological distress. To date, studies longitudinally testing for risk or protective factors of PSMU, and collecting qualitative data are still scarce among midadolescents. Self-help interventions specifically targeting PSMU in this population and involving midadolescents in co-creation are needed. OBJECTIVE: The 2-year SMART multicenter project aims to (1) advance knowledge on PSMU; (2) co-design an unguided self-help app for promoting awareness and functional SM use; and (3) test feasibility and provide preliminary findings on its effectiveness to further improve and adapt the app. METHODS: The SMART project is organized in 3 phases: phase 1 will focus on knowledge advancement on PSMU and its risk and protective factors using a longitudinal design; phase 2 will explore adolescents perspectives using qualitative approach and will co-design an unguided self-help app for reducing PSMU, which will be evaluated and adapted in phase 3. Around 1500 midadolescents (aged 14-18 years) will be recruited in northern, central, and southern Italy to investigate the potential intra- and interpersonal psychological risk and protective factors for PSMU and define specific PSMU profiles and test for its association with psychological distress. Subjective (self-report) PSMU's psychosocial risk or protective factors will be assessed at 3 different time points and Ecological Momentary Assessment (EMA) will be used. Moreover, focus groups will be performed in a subsample of midadolescents to collect the adolescents' unique point of view on PSMU and experiences with SM. Those previous results will inform the self-help app, which will be co-designed through working groups with adolescents. Subsequently, the SMART app will be deployed and adapted, after testing its feasibility and potential effectiveness in a pilot study. RESULTS: The project is funded by the Italian Ministry of University and Research as part of a national grant (PRIN, "Progetti di Rilevante Interesse Nazionale"). The research team received an official notice of research funding approval in July 2023 (Project Code 2022LC4FT7). The project was preregistered on Open Science Framework, while the ethics approval was obtained in November 2023. We started the enrollments in December 2023, with the final follow-up data to be collected within May 2025. CONCLUSIONS: The innovative aspects of the SMART project will deepen the conceptualization of PSMU and of its biopsychosocial antecedents among midadolescents, with relevant scientific, technological, and socioeconomic impacts. The advancement of knowledge and the developed self-help app for PSMU will timely respond to midadolescents' increased loneliness and psychological burden due to COVID-19 pandemic and humanitarian crisis. TRIAL REGISTRATION: OSF Registries; https://osf.io/2ucnk/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58739.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.033
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.010
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0330.009

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 head0.273
GPT teacher head0.546
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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
Published2024
Admission routes1
Has abstractyes

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