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Record W2923861466 · doi:10.1002/oby.22396

Meeting Report on the Conference on Fathers’ Role in Children’s Weight‐Related Behaviors and Outcomes

2019· article· en· W2923861466 on OpenAlexaffabout
Mwalu Peeters, Kirsten K. Davison, David W.L., Jess Haines

Bibliographic record

VenueObesity · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBest practicePsychologyHealth professionalsDevelopmental psychologyMedical educationMedicinePolitical scienceHealth care

Abstract

fetched live from OpenAlex

On September 22, 2017, "Engaging the Forgotten Parent: Conference of Experts on Fathers' Role in Children's Weight-Related Behaviours and Outcomes" brought researchers, students, health professionals, community stakeholders, and knowledge users from Canada, Australia, and the United States together to Toronto, Ontario, for a 1-day gathering focused on furthering scientific understanding of effective strategies to recruit, engage, and retain fathers in family-based obesity research. Furthermore this conference focused on identifying key knowledge gaps and opportunities for collaborative research to further our understanding of fathers' roles in the development of their children's behaviors and weight outcomes. The following four themes emerged from the conference: (1) underrepresentation of fathers in family-based obesity research, (2) fathers' engagement in children's weight-related behaviors and desire to participate in research, (3) best practices in recruiting, engaging, and retaining fathers in research, and (4) key lessons learned from fatherhood research in developmental science.

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)0.012
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0060.001
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0610.012

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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2019
Admission routes2
Has abstractyes

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