Agreement of antenatal care indicators from self-reported questionnaire and the antenatal care card of women in the 2015 Pelotas birth cohort, Rio Grande do Sul, Brazil
Bibliographic record
Abstract
BACKGROUND: Studies of healthcare service use during the pregnancy-postpartum cycle often rely on self-reported data. The reliability of self-reported information is often questioned as administrative data or medical records, such as antenatal care cards, are usually preferred. In this study, we measured the agreement of antenatal care indicators from self-reported information and antenatal care cards of pregnant women in the 2015 Pelotas Birth Cohort, Brazil. METHODS: In a sample of 3923 mothers, indicator agreement strengths were estimated from Kappa and prevalence-and-bias-adjusted Kappa (PABAK) coefficients. Maternal characteristics associated with indicator agreements were assessed with heterogeneity chi-squared tests. RESULTS: The self-reported questionnaire and the antenatal care card showed a moderate to high agreement in 10 of 21 (48%) antenatal care indicators that assessed care service use, clinical examination and diseases during pregnancy. Counseling indicators performed poorly. Self-reported information presented a higher frequency data and a higher sensitivity but slightly lower specificity when compared to the antenatal card. Factors associated with higher agreement between both data sources included lower maternal age, higher level of education, primiparous status, and being a recipient of health care in the public sector. CONCLUSIONS: Self-reported questionnaire and antenatal care cards provided substantially different information on indicator performance. Reliance on only one source of data to assess antenatal care quality may be questionable for some indicators. From a public health perspective, it is recommended that antenatal care programs use multiple data sources to estimate quality and effectiveness of health promotion and disease prevention in pregnant women and their offspring.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".