Maternal serum screening in Newfoundland and Labrador: do attitude and knowledge affect physicians' practice?
Bibliographic record
Abstract
OBJECTIVE: To examine family physicians' practice of, attitudes toward, and knowledge about maternal serum screening (MSS) and to compare the demographic and practice characteristics, attitudes, and knowledge of physicians who offer MSS to all their pregnant patients with those of physicians who offer MSS to some or none of their pregnant patients. DESIGN: Cross-sectional mailed survey. SETTING: Newfoundland and Labrador. PARTICIPANTS: One hundred eighty-two family physicians who provided prenatal care. MAIN OUTCOME MEASURES: Proportion of physicians offering MSS to their pregnant patients. Sociodemographic characteristics and attitudes toward and knowledge about MSS of physicians who offer MSS to all, some, or none of their pregnant patients. RESULTS: Just over half the physicians (52.2%) offered MSS to all their pregnant patients, 34.6% offered it to some patients, and 13.2% did not offer MSS at all. Almost two thirds of physicians (63.6%) had not changed their practice regarding MSS in the past 18 months, but 29.5% said they offered MSS more often. About 69.6% of physicians communicated positive results to patients within 48 hours; 60.8% communicated negative results at the next clinical appointment. Half (50.6%) believed that offering MSS did not affect their legal risk, 24.1% said it increased their risk, and 25.3% said it decreased their risk. Most physicians (83.4%) ordered MSS at the correct gestational age. A larger proportion of those who offered MSS to all patients were female, were between 30 and 39 years old, had graduated from Canadian medical schools, practised in urban centres, and were aware of the provincial MSS program. Physicians who offered MSS to all, some, or none of their patients were similar in terms of length of practice in Canada, whether they performed deliveries, number of pregnant women they cared for annually, beliefs about MSS and legal risk, and general knowledge of MSS detection rates. CONCLUSION: More than half the family physicians in Newfoundland and Labrador offered MSS to all their pregnant patients, and another third offered it to some patients. Physicians' practice was not related to their attitudes toward or knowledge about MSS.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".