Chlamydia trachomatis infection during pregnancy: Knowledge, test practices, and attitudes of Dutch midwives
Bibliographic record
Abstract
BACKGROUND: Chlamydia trachomatis infection in pregnancy may lead to adverse pregnancy outcomes. In the Netherlands, testing for C. trachomatis is based on risk assessment. We assessed midwives' knowledge, test practices, assessment of risk behavior, and attitudes regarding testing for C. trachomatis infection during pregnancy. We evaluated the association between midwives' characteristics and their knowledge of C. trachomatis infection in terms of symptomatology and outcomes. METHODS: This was a cross-sectional study among primary care midwives in the Netherlands. Between September and November 2011, midwives from all Dutch primary care midwifery practices were invited to complete a questionnaire about C. trachomatis infection. RESULTS: Of the 518 midwives invited to participate in this study, 331 (63.9%) responded. The overall median knowledge score for questions about symptomatology and outcomes was 10 out of a maximum score of 15. The median knowledge score was higher among midwives in urban areas. In total, 239 (72.2%) midwives reported testing pregnant women for C. trachomatis. The primary reason for testing was a request by the woman herself (96.2%), followed by symptoms of infection (89.1%), risk behavior (59.3%), and risk factors for infection (7.3%). Almost 25% of midwives showed positive attitudes towards universal screening for C. trachomatis. CONCLUSIONS: Midwives were knowledgeable about symptoms of infection, but less about outcomes. Midwives test pregnant women for C. trachomatis mainly on the women's request. Otherwise, testing is based on symptoms of infection rather than on known risk factors. This may contribute to under-diagnosis and under-treatment, leading to maternal, perinatal, and neonatal morbidity.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".