Laying the groundwork for prenatal dietary assessment research among First Nations women at risk for alcohol use: Implications for Fetal Alcohol Spectrum Disorder
Bibliographic record
Abstract
Fetal Alcohol Spectrum Disorder (FASD) is a health concern that is over-represented among First Nations peoples. Optimal prenatal nutrition plays a role in the severity of FASD. Prenatal nutrition as it relates to fetal brain development and fetal alcohol exposure is an under-researched area, especially among pregnant First Nations women. Finding current dietary intake patterns of pregnant women who drink alcohol could lead to developing a nutrition provision strategy. However, there is no appropriate dietary assessment research tool that is specific to this population. This study aims to develop an effective, culturally appropriate and interactive dietary assessment research tool using participatory methods to engage with women and communities in the process. We used community health priorities forums, information sessions, volunteering, collaboration with programs, and a trauma-informed approach as methods to engage with pregnant women. To develop the research tool, top sources of fetal brain development nutrients were determined for the food frequency component, several prenatal health workers reviewed the tool, and a pre-test with 20 pregnant women of the target population was completed. Pre-test results show the tool is being well-received. All of this ground work will help pave a path for further prenatal nutrition research with First Nations women. This research will inform programs and policies which strive to improve food and nutrition security and reduce the severity of FASD.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".