Examining the scope of public health nursing practice in sexually transmitted infection prevention and management: what do nurses do?
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To develop a more comprehensive understanding of the scope of public health nursing practice in the prevention and management of sexually transmitted infections and also to examine the public health nursing workforce in sexually transmitted infection care and the range of patient populations served. BACKGROUND: Sexually transmitted infections are increasing, widespread and remain a significant public health problem throughout the world. Although nurses are taking on expanded roles in sexually transmitted infection care, little is known about the scope of this practice. DESIGN: A cross-sectional descriptive study took place over 18 months (2009-2010). METHODS: Three hundred and fourteen eligible nurses completed a 62-item questionnaire. RESULTS: 93·6% of participants were women; 77·5% were baccalaureate prepared and 87·9% underwent continuing education in sexually transmitted infection care. Most spent 50% of their time in direct patient care. Women were the main care recipients (72·9%). Sexually transmitted infection care was one aspect of nurses' multifaceted public health roles accounting for 28% of overall work activities. Not all nurses were working to the full scope of their practice; 77·9% undertook health history assessment, and 79% conducted testing. CONCLUSION: This study is a comprehensive description of the scope of sexually transmitted infection nursing practice activities. It expands our understanding of sexually transmitted infection nursing practice among nurses working within an expanded scope and provides a baseline for future investigations. This description is situated within nursing competencies and best practices that may be used to develop, implement and evaluate models of sexually transmitted infection service delivery in other locales. CLINICAL PRACTICE RELEVANCE: Sexually transmitted infection nursing practice needs to be understood and investigated beyond health education and testing practices. The scope of practice is comprehensive and incorporates a full spectrum of care. Public health nurses are a critical entry point into the healthcare system and provide primary and preventative care and healthcare referrals. Models of nursing care need to support nurses working to their full scope, and associated barriers warrant further investigation.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".