Primary care nurses’ knowledge on Zika virus infection during pregnancy
Bibliographic record
Abstract
Cases related to Zika Virus infections in Brazil have become a severe public health problem due to its relation to microcephaly and other neurological and development problems in newborn babies of mothers who were infected by the Zika virus. Nurses are vital professionals in combating this infection, both for prevention and vector control; as well as handling the target public. Therefore, the research aimed to investigate Primary Care nurses’ knowledge on Zika virus infection during pregnancy. It is a descriptive field study with qualitative approach carried out at Family Health Strategy, in the city of Cajazeiras, Paraíba, Brazil. Semi-structured interviews were conducted for data collection, which were submitted to thematic-content analysis. The nurses replied that there is not much information about Zika virus infections, and reported brain impairment as the main consequence for the newborn baby. Nurses also revealed that many women of the community showed negative feelings towards the pregnancy, and they pointed out the importance of health education actions in the community for dissemination of information on Zika virus infection control and prevention. Nurses’ contribution regarding the assistance provided to the community is considered satisfactory. However, there was a need to bring efficient and updated professional training to the reality of each community. Therefore, it is suggested that training be provided through continued health education to professionals.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".