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Record W4414321025 · doi:10.54434/candj.214

Supporting Perinatal Health in Individuals with Obesity: Integrative and Naturopathic Perspectives

2025· article· en· W4414321025 on OpenAlexvenueno aff
Alexsia Priolo, Kelsey Hroch, Meghan McNaughton

Bibliographic record

VenueCAND Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsBreastfeedingPsychological interventionPregnancyObesityPostpartum periodWeight managementMental healthPublic health

Abstract

fetched live from OpenAlex

Obesity in the perinatal period represents a growing public health concern with significant implications for short- and long-term maternal and fetal outcomes. This review summarizes the current recommendations related to perinatal obesity, exploring evidence-informed strategies for prevention and management from the preconception to the postpartum period. In the preconception period, this review addresses weight management, lifestyle counselling, targeted supplementation, and behaviour strategies through motivational interviewing. During pregnancy, strategies to support optimal gestational weight gain, evidence-based supplementation to mitigate risks of complications such as preeclampsia and gestational diabetes, and lifestyle interventions aimed at reducing obesity-related pregnancy risks are reviewed. Lastly, in the postpartum period, this review examines the impact of maternal obesity on early recovery, breastfeeding initiation, and mental health, as well as the long-term risks of postpartum weight retention and strategies to support sustainable, values-aligned lifestyle changes. Future directions should prioritize interdisciplinary collaboration, including obstetrics, primary care, nutrition, behavioural health, and community-based support systems. Research is also needed to refine diagnostic criteria, evaluate the long- term effects of prenatal interventions, and ensure equitable care for populations disproportionately affected by obesity and its sequelae.

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 score0.007
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.312
Teacher spread0.304 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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