Health care providers’ recommendations and influenza vaccination during pregnancy: A Québec survey
Bibliographic record
Abstract
Background: Seasonal influenza is associated with high morbidity and mortality among pregnant women. Inactivated influenza vaccine is recommended for all pregnant women. Our study evaluated health care providers’ compliance with current Québec guidelines and their estimation of vaccine acceptance among the pregnant women they care for. Methods: An electronic survey was distributed to 1,084 health care providers, including general practitioners (GP), obstetrician-gynecologists (OBGYN), midwives, and nurse practitioners (NP), in Québec who followed at least one pregnant woman in the past year and who could be reached by their professional association by email. The survey was available online (via SurveyMonkey) between January 15 and February 15, 2015. Results: A total of 344/1,084 health care providers (32%) answered the survey: 134/250 GPs (54%), 91/497 OBGYNs (18%), 76/186 midwives (40%), and 43/150 NPs (29%). Overall, 60% of participants reported following current recommendations. Provider’s age <40 years old (54% versus 67%; p=0.02), low number of pregnancies followed per year (<50: 35% versus ≥50: 73%; p<0.001), and having a non-academic practice (54% vs 66%; p=0.03) were associated with lower compliance with guidelines. Compliance with vaccination recommendations differed between professions (OBGYNs: 80%, NPs: 72%, GPs: 67%, midwives: 12%; p<0.001). According to participants, about 40% of women accept vaccination when offered, and their reasons for refusing vaccination were mostly efficacy and safety issues. Conclusions: Québec faces major barriers to influenza vaccination of pregnant women: health care professionals report low compliance with guidelines, and they perceive and report that women are reluctant to accept vaccination. Action should be taken to increase vaccination offers by health care workers to pregnant women, with the aim of increasing the influenza immunization rate in this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".