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Record W4294762418 · doi:10.1111/ijpo.12973

<scp>SMART</scp> goals of children of 6–12 years enrolled in a family‐centred lifestyle intervention for childhood obesity: Secondary analysis of a randomized controlled trial

2022· article· en· W4294762418 on OpenAlexafffund
Alysha L. Deslippe, Andy Bains, Sarah‐Eve Loiselle, Popi Kasvis, Ivy Lynn Mak, Hope A. Weiler, Tamara R. Cohen

Bibliographic record

VenuePediatric Obesity · 2022
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMcGill University Health CentreBC Children's HospitalMcGill UniversityUniversity of British Columbia
FundersDairy Farmers of Canada
KeywordsMedicineChildhood obesityPsychological interventionIntervention (counseling)Body mass indexNorm (philosophy)Theory of planned behaviorObesityRandomized controlled trialGerontologyPhysical activityPhysical therapyOverweightControl (management)Psychiatry

Abstract

fetched live from OpenAlex

This study demonstrates how SMART (Specific, Measurable, Attainable, Realistic and Timely) goals set by children in a lifestyle intervention contributed favorably to weight outcomes. Children (6-12 years) set goals with a registered dietitian over six months. Goals were classified according to their type (diet or activity), direction, (increase healthy or decrease unhealthy), and theoretical constructs. Theoretical constructs included the Theory of Planned Behavior's attitudes (i.e., changing beliefs about behaviour outcomes), subjective norm (i.e., incorporation of health recommendations) and perceived behavioural control (i.e., over goal barriers and facilitators). Constructs from a Socio-Ecological Model (family or individual) were also applied. Participants who maintained or decreased their body mass index for-age-and-sex z-scores (BAZ) after six months created significantly more goals related to the subjective norm compared to those whose BAZ increased (p = 0.003). Future interventions using SMART goals should incorporate health recommendations (i.e., the subjective norm) through actionable items among children to promote success.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.300
Teacher spread0.285 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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