[Multivitamin supplement for primary prevention of birth defects: application of a preventive clinical practice].
Bibliographic record
Abstract
OBJECTIVE: To determine whether the recommendations health care professionals make to women of childbearing age on the importance of taking folic acid encourage these women to take folic acid supplements. DESIGN: Survey. SETTING: The Centre de santé et de services sociaux at the Institut universitaire de gériatrie de Sherbrooke in Sherbrooke, Que. PARTICIPANTS: A total of 323 Francophone women 18 to 45 years old. MAIN OUTCOME MEASURES: Whether or not women had consumed vitamin and mineral supplements during the past year. Descriptive, bivariate statistical analyses and logistic regression modeling were carried out to determine whether the association between health care professionals' recommendations and the consumption of vitamin and mineral supplements persisted after controlling for certain variables (consulting documentation, knowledge, sources of information, perception, age, education, income, marital status, and plans to become pregnant). RESULTS: About 41% of the women reported that their physicians had recommended that they take vitamin and mineral supplements. After adjusting for all the variables in the model, it became clear that there was a significant association between the recommendations of healthcare professionals and the consumption of vitamins and minerals by women of childbearing age. CONCLUSION: Health care professionals can improve the health of the population through preventive clinical practices. It is important that we support them in their efforts to integrate and apply scientific knowledge in their practice.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".