Practice Nurse Provision of Long‐Acting Reversible Contraception: A Cross‐Sectional Survey of Knowledge and Practices
Bibliographic record
Abstract
AIM: To describe practice nurse long-acting reversible contraception (LARC) knowledge and practices. DESIGN: Cross-sectional survey. METHODS: Between July and December 2021, we conducted an online survey using convenience sampling to recruit Australian nurses who work in primary care, known as practice nurses. We collected data about demographics and knowledge and practices relating to LARC. Analysis used descriptive statistics and Poisson regression. RESULTS: From 489 eligible responses, most respondents were women and the majority worked in metropolitan practices. Most (90.4%) believed that their advice could influence women's contraceptive choices. Few inserted/removed intrauterine devices (IUDs) (11.2%) or implants (15.9%). Of those that did insert LARC, most did so one to five times in the last month (IUDs 72.2%; implants 73.6%). General practice as a primary place of work was negatively associated with implant provision. Respondents with more general practice experience (≥ 15 years) and/or higher qualifications were more likely to respond correctly to knowledge questions and provide IUDs or implants. Most (62.8%) correctly identified IUD suitability for nulliparous women. CONCLUSIONS: Practice nurses have knowledge gaps and limited practice opportunities for LARC provision. IMPLICATIONS: Practice nurses need supportive funding policies and ongoing education and skills development to enhance patient access to LARC and their choice of provider. REPORTING METHOD: CHERRIES guideline. PATIENT OR PUBLIC CONTRIBUTION: Partner organisations assisted with the study's recruitment. TRIAL REGISTRATION: ACTRN12622000655741.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".