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Record W4306177252 · doi:10.1097/nt.0000000000000565

Barriers to Implementing Weight Management Recommendations

2022· article· en· W4306177252 on OpenAlexaff
Kevin Metcalf, Lauri Wright, Andrea Y. Arikawa, Jenifer Ross

Bibliographic record

VenueNutrition Today · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsWeight managementMedicineReferralBest practicePsychological interventionNursingObesityFamily medicineMedical educationWeight lossManagementPathology

Abstract

fetched live from OpenAlex

Dietitians are responsible for using evidence-based practice to mitigate the effects of obesity; however, it is unclear how dietitians use research to guide weight management interventions. The aim of this pilot study was to identify the barriers of research utilization and implementation of evidence-based practice in adult weight management. A survey was disseminated to dietitians working at least part-time with people with obesity. Dietitians seem to value research and evidence-based practice; however, implementation may be an issue. The pilot study found that workplace setting may provide a barrier to research utilization, but dietitian opinion of current screening and referral guidelines may also be a significant barrier to implementing best practices in adult weight management.

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)0.067
metaresearch head score (Gemma)0.236
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.236
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.044
GPT teacher head0.401
Teacher spread0.358 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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