Evaluating implementation of the <scp>FIGO</scp> Nutrition Checklist for preconception and pregnancy within the <i>Bukhali</i> trial in Soweto, South Africa
Bibliographic record
Abstract
OBJECTIVE: To evaluate implementation of the FIGO Nutrition Checklist in a low/middle-income South African setting. METHODS: This is a mixed-methods study. Following administration of the FIGO Nutrition Checklist by a dietitian between July 2021 and May 2022, quantitative responses from pregnant (n = 96) and nonpregnant (n = 291) participants with overweight or obesity were analyzed, using logistic regression. Qualitative data from in-depth interviews with the dietitian and a subgroup of participants (n = 15) were analyzed using reflexive thematic analysis. RESULTS: Of 387 participants, 97.4% (n = 377) answered 'no' to at least one diet quality question on the FIGO Nutrition Checklist, indicative of an at-risk dietary practice. Food insecurity was positively associated with having more than three at-risk practices (OR 1.87; 95% CI, 1.10-3.18; P = 0.021). Themes from the dietitian interview included ease of use of the checklist; required adaptations to it, including explanation and translation; and benefits of the tool. Despite challenges to healthy nutrition, participant interviews identified that the checklist is acceptable and supported improved awareness of dietary intakes. CONCLUSION: Considering the high incidence of at-risk dietary practices identified by the FIGO Nutrition Checklist in this population, further research into use of the tool across South African healthcare settings is warranted.
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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 | 0.086 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".