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Record W4311677376 · doi:10.1186/s12966-022-01378-x

Cook like a Boss Online: an adapted intervention during the COVID-19 pandemic that effectively improved children’s perceived cooking competence, movement competence and wellbeing

2022· article· en· W4311677376 on OpenAlexfundno aff
Lynsey Hollywood, Johann Issartel, David Gaul, Amanda McCloat, Elaine Mooney, Clare E. Collins, Fiona Lavelle

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2022
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsnot available
FundersMedical Research CouncilNational Health and Medical Research CouncilQueen's UniversityUlster UniversityQueen's University Belfast
KeywordsCompetence (human resources)Psychological interventionPsychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has further exacerbated physical inactivity, poor dietary intake and reduced mental wellbeing, contributing factors to non-communicable diseases in children. Cooking interventions are proposed as having a positive influence on children's diet quality. Motor skills have been highlighted as essential for performance of cooking skills, and this movement may contribute to wellbeing. Additionally, perceived competence is a motivator for behaviour performance and thus important for understanding intervention effectiveness. Therefore, this research aimed to assess the effectiveness of an adapted virtual theory-based cooking intervention on perceived cooking competence, perceived movement competence and wellbeing. METHODS: The effective theory-driven and co-created 'Cook Like A Boss' was adapted to a virtual five day camp-styled intervention, with 248 children across the island of Ireland participating during the pandemic. Pre- and post-intervention assessments of perceived cooking competence, perceived movement competence and wellbeing using validated measurements were completed through online surveys. Bivariate Correlations, paired samples t-tests and Hierarchical multiple regression modelling was conducted using SPSS to understand the relationships between the variables and the effect of the intervention. RESULTS: 210 participants had matched survey data and were included in analysis. Significant positive correlations were shown between perceived cooking competence, perceived movement competence and wellbeing (P < 0.05). Children's perceived cooking competence (P < 0.001, medium to large effect size), perceived movement competence (P < 0.001, small to medium effect size) and wellbeing (P = 0.013, small effect size) all significantly increased from pre to post intervention. For the Hierarchical regression, the final model explained 57% of the total variance in participants' post-intervention perceived cooking competence. Each model explained a significant amount of variance (P < 0.05). Pre-intervention perceived cooking competence, wellbeing, age and perceived movement competence were significant predictors for post-intervention perceived cooking competence in the final model. CONCLUSION: The 'Cook Like A Boss' Online intervention was an adapted virtual outreach intervention. It provides initial evidence for the associations between perceived cooking competence, perceived movement and wellbeing as well as being effective in their improvement. This research shows the potential for cooking to be used as a mechanism for targeting improvements in not only diet quality but also movement and wellbeing. TRIAL REGISTRATION: NCT05395234. Retrospectively registered on 26th May 2022.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.041
GPT teacher head0.349
Teacher spread0.308 · 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 designNon-randomized trial
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

Citations12
Published2022
Admission routes1
Has abstractyes

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