MétaCan
Menu
Back to cohort
Record W3169302191 · doi:10.2196/27958

Building Primary-School Children’s Resilience through a Web-Based Interactive Learning Environment: Quasi-Experimental Pre-Post Study

2021· article· en· W3169302191 on OpenAlexvenueno aff
Iolie Nicolaidou, Evi X. Stavrou, Georgia Leonidou

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPsychologyAnxietyMental healthApplied psychologyUsabilityLearning environmentPsychological resilienceMedical educationClinical psychologyMedicineSocial psychologyComputer scienceMathematics educationPsychotherapist

Abstract

fetched live from OpenAlex

Background Resilience is a person’s mental ability to deal with challenging situations adaptively and is a crucial stress management skill. Psychological resilience and finding ways to cope in crises is a highly relevant topic considering the COVID-19 pandemic, which enforced quarantine, social distancing measures, and school closures worldwide. Parents and children are currently living with increased stress due to COVID-19. We need to respond with immediate ways to strengthen children’s resilience. Internet-based cognitive behavioral therapy interventions for children's stress management overcome accessibility issues such as the inability to visit mental health experts owing to COVID-19 movement restrictions. An interactive learning environment was created, based on the preventive program “Friends,” to overcome accessibility issues associated with delivering cognitive behavioral therapy–based interventions in formal and informal education settings. Objective This study aimed to examine the effectiveness of a web-based learning environment on resilience in (1) reducing anxiety symptoms and (2) increasing emotion recognition and recognition of stress management techniques among 9-10-year-old children. We also aimed to evaluate the learning environment’s usability. Methods A quasi-experimental pretest-posttest control group design was used. In total, 20 fourth graders in the experimental group interacted with the learning environment over 6 weekly 80-minute sessions. Further, 21 fourth graders constituted the control group. The main data sources were (1) a psychometric tool to measure children’s anxiety symptoms, namely the Greek translation of the original Spence Children’s Anxiety Scale, (2) 3 open-ended questions assessing emotion recognition and recognition of stress management techniques, and (3) the System Usability Scale to measure the usability of the learning environment. Results In both groups, there was a small but nonsignificant postintervention reduction in reported anxiety symptoms, except for obsessive-compulsive disorder symptoms in the experimental group. A paired samples t test revealed that students’ reported symptom scores of obsessive-compulsive disorder significantly decreased from 1.06 (SD 0.68) to 0.76 (SD 0.61) (t19= 5.16; P=.01). The experimental group revealed a significant increase in emotion recognition (t19=–6.99; P<.001), identification of somatic symptoms of stress (t19=–7.31; P<.001), and identification of stress management techniques (t19=–6.85; P<.001). The learning environment received a satisfactory usability score. The raw average system usability score was 76.75 (SD 8.28), which is in the 80th percentile rank and corresponds to grade B. Conclusions This study shows that interactive learning environments might deliver resilience interventions in an accessible and cost-effective manner in formal education, potentially even in distance-learning conditions owing to the COVID-19 pandemic. Interactive learning environments on resilience are also valuable tools for parents who can use them with their children at home, for informal learning, using mobile devices. As such, they could be a promising first-step, low-intensity intervention that children and the youth can easily access.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.016
GPT teacher head0.348
Teacher spread0.332 · 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.

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

Citations17
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Pediatrics and ParentingSame topicDigital Mental Health InterventionsFrench-language works237,207