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Record W3197783747 · doi:10.2196/31041

A Web-Based Risk-Reframing Intervention to Influence Early Childhood Educators’ Attitudes and Supportive Behaviors Toward Outdoor Play: Protocol for the OutsidePlay Study Randomized Controlled Trial

2021· article· en· W3197783747 on OpenAlexaffvenue
Mariana Brussoni, Christina Han, John Jacob, Fritha Munday, Megan Zeni, Melanie Walters, Tina Cheng, Amy Schneeberg, Emily Fox, Eva Oberle

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsBC Children's HospitalLearning PartnershipUniversity of British Columbia
Fundersnot available
KeywordsCognitive reframingIntervention (counseling)Randomized controlled trialPsychologyFocus groupProtocol (science)Applied psychologyIntervention mappingBehavior changeMedical educationMedicineNursingHealth promotionSocial psychologyPublic healthMarketingBusinessAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Early learning and childcare centers (ELCCs) can offer young children critical opportunities for quality outdoor play. There are multiple actual and perceived barriers to outdoor play at ELCCs, ranging from safety fears and lack of familiarity with supporting play outdoors to challenges around diverse perspectives on outdoor play among early childhood educators (ECEs), administrators, licensing officers, and parents. OBJECTIVE: Our study objective is to develop and evaluate a web-based intervention that influences ECEs' and ELCC administrators' perceptions and practices in support of children's outdoor play at ELCCs. METHODS: The development of the fully automated, open-access, web-based intervention was guided by the intervention mapping process. We first completed a needs assessment through focus groups of ECEs, ELCC administrators, and licensing officers. We identified key issues, needs, and challenges; opportunities to influence behavior change; and intervention outcomes and objectives. This enabled us to develop design objectives and identify features of the OutsidePlay web-based intervention that are central to addressing the issues, needs, and challenges of ECEs and ELCC administrators. We used social cognitive theory and behavior change techniques to select methods, applications, and technology to deliver the intervention. We will use a two-parallel-group randomized controlled trial (RCT) design to evaluate the efficacy of the intervention. We will recruit 324 ECEs and ELCC administrators through a variety of web-based means, including Facebook advertisements and mass emails through our partner networks. The RCT study will be a purely web-based trial where outcomes will be self-assessed through questionnaires. The RCT participants will be randomized into the intervention group or the control group. The control group participants will read the Position Statement on Active Outdoor Play. RESULTS: The primary outcome is increased tolerance of risk in children's play, as measured by the Teacher Tolerance of Risk in Play Scale. The secondary outcome is self-reported attainment of a self-developed behavior change goal. We will use mixed effects models to test the hypothesis that there will be a difference between the intervention and control groups with respect to tolerance of risk in children's play. Differences in goal attainment will be tested using logistic regression analysis. CONCLUSIONS: The OutsidePlay web-based intervention guides users through a personalized journey that is split into 3 chapters. An effective intervention that addresses the barriers to outdoor play in ELCC settings has the potential to improve children's access to outdoor play and support high-quality early childhood education. TRIAL REGISTRATION: ClinicalTrials.gov NCT04624932; https://clinicaltrials.gov/ct2/show/NCT04624932. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31041.

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)0.024
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.071
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.022
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0120.004
Bibliometrics0.0030.003
Science and technology studies0.0040.003
Scholarly communication0.0040.004
Open science0.0040.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0710.010

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 head0.079
GPT teacher head0.494
Teacher spread0.415 · 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 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

Citations7
Published2021
Admission routes2
Has abstractyes

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