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Record W4400557890 · doi:10.2196/63220

Effects of a Parent-Child Single-Session Growth Mindset Intervention on Adolescent Depression and Anxiety Symptoms: Protocol of a 3-Arm Waitlist Randomized Controlled Trial

2024· article· en· W4400557890 on OpenAlexvenueno aff
Shimin Zhu, Yuxi Hu, Di Qi, Paul H. Lee, So Wa Ngai, Qijin Cheng, Paul Wai Ching Wong

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialMindsetAnxietyIntervention (counseling)Session (web analytics)Clinical psychologyPsychologyDepression (economics)Protocol (science)PsychiatryMedicinePhysical therapyAlternative medicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Depression and anxiety are common mental health problems among adolescents worldwide. Extant research has found that intelligence, emotion, and failure-is-debilitating beliefs (fixed mindsets) are closely related to more depression and anxiety symptoms, hopelessness, and suicidality. Recent research also points to the importance of parental mindset, which can strongly influence children's affect, behavior, and mental health. However, the effects of parent-child mindset interventions on a child's internalizing problems have not yet been empirically examined. As recent evidence has shown the promise of single-session interventions in reducing and preventing youth internalizing problems, this study develops and examines a parent and child single-session intervention on mindsets of intelligence, failure, and emotion (PC-SMILE) to tackle depression and anxiety in young people. OBJECTIVE: Using a 3-arm randomized controlled trial, this study will examine the effectiveness of PC-SMILE in reducing depression and anxiety symptoms among children. We hypothesize that compared to the waitlist control group, the PC-SMILE group and child single-session intervention on mindsets of intelligence, failure, and emotion (C-SMILE) group will significantly improve child depression and anxiety (primary outcome) and significantly improve secondary outcomes, including children's academic self-efficacy, hopelessness, psychological well-being, and parent-child interactions and relationships, and the PC-SMILE is more effective than the C-SMILE. METHODS: A total of 549 parent-child dyads will be recruited from 8 secondary schools and randomly assigned to either the PC-SMILE intervention group, the C-SMILE intervention group, or the no-intervention waitlist control group. The 45-minute interventions include parent-version and child-version. Both parents and students in the PC-SMILE group receive the intervention. Students in C-SMILE group receive intervention and their parents will receive intervention after all follow-up ends. Students in 3 groups will be assessed at 3 time points, baseline before intervention, 2 weeks post intervention, and 3 months post intervention, and parents will be assessed in baseline and 3-month follow-up. The intention-to-treat principle and linear-regression-based maximum likelihood multilevel models will be used for data analysis. RESULTS: Recruitment started in September 2023. The first cohort of data collection is expected to begin in May 2024 and the second cohort will begin in September 2024. The final wave of data is expected to be collected by the end of the first quarter of 2025. The results are expected to demonstrate improved anxiety and depression among students assigned to the intervention condition, as well as the secondary outcomes compared to those in the control group. The efficacy and effectiveness of the intervention will be discussed. CONCLUSIONS: This study is the first attempt to develop a web-based single-session intervention for students and their parents to enhance their well-being in Hong Kong and beyond, which potentially contributes to providing evidence-based recommendations for the implementation of brief digital parent-child interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63220.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.212
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.076
GPT teacher head0.506
Teacher spread0.430 · 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 designRandomized trial
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

Citations4
Published2024
Admission routes1
Has abstractyes

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