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Record W2550551649 · doi:10.1111/ijpo.12200

A brief eHealth tool delivered in primary care to help parents prevent childhood obesity: a randomized controlled trial

2016· article· en· W2550551649 on OpenAlexafffund
Jillian L.S. Byrne, T. Cameron Wild, Katerina Maximova, Nadia E. Browne, Nicholas L. Holt, Andrew Cave, Pamela Martz, C. Ellendt, Geoff D.C. Ball

Bibliographic record

VenuePediatric Obesity · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsAlberta Health ServicesAlberta HealthGovernment of AlbertaUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineBrief interventioneHealthChildhood obesityPsychological interventionReferralFamily medicinePrimary careRandomized controlled trialObesityHealth carePediatricsOverweightNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the feasibility and preliminary impact of an electronic health (eHealth) screening, brief intervention and referral to treatment (SBIRT) delivered in primary care to help parents prevent childhood obesity. METHODS: Parents of children (5-17 years) were recruited from a primary care clinic. Children's measured height and weight were entered into the SBIRT on a study-designated tablet. The SBIRT screened for children's weight status, block randomized parents to one of four brief interventions or an eHealth control and provided parents with a menu of optional obesity prevention resources. Feasibility was determined by parents' interest in, and uptake of, the SBIRT. Preliminary impact was based on parents' concern about children's weight status and intention to change lifestyle behaviours post-SBIRT. RESULTS: Parents (n = 226) of children (9.9 ± 3.4 years) were primarily biological mothers (87.6%) and Caucasian (70.4%). The proportion of participants recruited (84.3%) along with parents who selected optional resources within the SBIRT (85.8%) supported feasibility. Secondary outcomes did not vary across groups, but non-Caucasian parents classified as inaccurate estimators of children's weight status reported higher levels of concern and intention to change post-SBIRT. CONCLUSIONS: Our innovative, eHealth SBIRT was feasible in primary care and has the potential to encourage parents of unhealthy weight children towards preventative action.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.247
Teacher spread0.239 · 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 designRandomized 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

Citations18
Published2016
Admission routes2
Has abstractyes

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