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Record W4416592952 · doi:10.3389/fnut.2025.1628853

Barriers and facilitators to lifestyle behavior change and goal setting in adolescents with polycystic ovary syndrome

2025· article· en· W4416592952 on OpenAlexfundno aff
Manasa Gadiraju, Joy Y. Kim, Erin M. Green, Alexandra MacMillan Uribe, Tania S. Burgert, Melissa D. Olfert, Heidi Vanden Brink

Bibliographic record

VenueFrontiers in Nutrition · 2025
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchTexas A and M University
KeywordsGoal settingAffect (linguistics)ObesitySet (abstract data type)Behavior changePolycystic ovaryLife styleHealth behaviorPhysical activity

Abstract

fetched live from OpenAlex

Purpose: To identify barriers and facilitators to lifestyle modifications and goal setting and characterize goal setting for adolescents with polycystic ovary syndrome (PCOS). Methods: objectives and characterized for emerging themes using qualitative content analysis. Results: Five major themes of barriers and facilitators to behavior change emerged: interest and motivation, family involvement, resources and food environment, taste preferences, and self-efficacy. RDNs set ≥3 goals with 52%, 2 goals with 35%, 1 goal with 9%, and no goals with 3% of adolescents with PCOS. Goals covered three major themes: incorporation of the United States Department of Agriculture (USDA) MyPlate model, modifying carbohydrate intake, and increasing physical activity. Conclusion: The facilitators and barriers identified through our analysis are both similar and different to those reported in adolescents with obesity and women with PCOS, likely due to differences in condition specific contexts and life stage. The goals recorded by RDNs reflect a desire to increase diet quality; however, too many goals may have been set on average. Overall, adolescents with PCOS report intrapersonal, interpersonal, and environmental barriers and facilitators that may affect their ability to establish and act upon goals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.234
Teacher spread0.227 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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