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Record W2313368737 · doi:10.5993/ajhb.38.4.9

Group-Based Lifestyle Sessions for Gestational Weight Gain Management: A Mixed Method Approach

2014· article· en· W2313368737 on OpenAlexaff
Samantha M. Harden, Mark R. Beauchamp, Brian Pitts, Edith M Nault, Brenda M. Davy, Wen You, Patrice L. Weiss, Paul A. Estabrooks

Bibliographic record

VenueAmerican Journal of Health Behavior · 2014
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeneralizability theoryWeight gainWeight managementPrenatal careMedicinePreferenceFamily medicinePsychologyNursingWeight lossObesityBody weightDevelopmental psychologyPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: To integrate group-based lifestyle sessions (GBLS) within prenatal care for gestational weight gain (GWG) management. METHODS: In Study 1, participants attended GBLS during prenatal care visits. Participants in Study 2 attended off-site GBLS whereby care providers were asked to discuss the program with patients. Process and outcome evaluation were conducted through a mixed-methods approach. RESULTS: In both pre-experimental feasibility studies, data provide preliminary support for GBLS (eg, positive care provider and patient feedback, weight gain patterns) as well as highlight areas for future research (eg, lack of GWG management discussions, preference for GBLS location). CONCLUSIONS: GBLS represents a promising approach to GWG management. Future research should assess the generalizability, sustainably, and compatibility of GBLS within prenatal care.

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)0.014
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.028
GPT teacher head0.379
Teacher spread0.352 · 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 designQualitative
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

Citations31
Published2014
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Health BehaviorSame topicGestational Diabetes Research and ManagementFrench-language works237,207