Addressing embodied inequities in health: how do we enable improvement in women’s diet in pregnancy?
Bibliographic record
Abstract
OBJECTIVE: To disrupt cycles of health inequity, traceable to dietary inequities in the earliest stages of life, public health interventions should target improving nutritional wellbeing in preconception/pregnancy environments. This requires a deep engagement with pregnant/postpartum people (PPP) and their communities (including their health and social care providers, HSCP). We sought to understand the factors that influence diet during pregnancy from the perspectives of PPP and HSCP, and to outline intervention priorities. DESIGN: We carried out thematic network analyses of transcripts from ten focus group discussions (FGD) and one stakeholder engagement meeting with PPP and HSCP in a Canadian city. Identified themes were developed into conceptual maps, highlighting local priorities for pregnancy nutrition and intervention development. SETTING: FGD and the stakeholder meeting were run in predominantly lower socioeconomic position (SEP) neighbourhoods in the sociodemographically diverse city of Hamilton, Canada. PARTICIPANTS: All local, comprising twenty-two lower SEP PPP and forty-three HSCP. RESULTS: Salient themes were resilience, resources, relationships and the embodied experience of pregnancy. Both PPP and HSCP underscored that socioeconomic-political forces operating at multiple levels largely determined the availability of individual and relational resources constraining diet during pregnancy. Intervention proposals focused on cultivating individual and community resilience to improve early-life nutritional environments. Participants called for better-integrated services, greater income supports and strengthened support programmes. CONCLUSIONS: Hamilton stakeholders foregrounded social determinants of inequity as main factors influencing pregnancy diet. They further indicated a need to develop interventions that build resilience and redistribute resources at multiple levels, from the household to the state.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".