What do Australian primary care clinicians need to provide long-acting reversible contraception and early medical abortion? A content analysis of a virtual community of practice
Bibliographic record
Abstract
BACKGROUND: Uptake of long-acting reversible contraception (LARC) is lower in Australia compared with other high-income countries, and access to early medical abortion (EMA) is variable with only 11% of general practitioners (GPs) providing EMA. The AusCAPPS (Australian Contraception and Abortion Primary Care Practitioner Support) Network is a virtual community of practice established to support GPs, nurses and pharmacists to provide LARC and EMA in primary care. Evaluating participant engagement with AusCAPPS presents an opportunity to understand clinician needs in relation to LARC and EMA care. METHODS: Data were collected from July 2021 until July 2023. Numbers of online resource views on AusCAPPS were analysed descriptively and text from participant posts underwent qualitative content analysis. RESULTS: In mid-2023 AusCAPPS had 1911 members: 1133 (59%) GPs, 439 (23%) pharmacists and 272 (14%) nurses. Concise point-of-care documents were the most frequently viewed resource type. Of the 655 posts, most were created by GPs (532, 81.2%), followed by nurses (88, 13.4%) then pharmacists (16, 2.4%). GPs most commonly posted about clinical issues (263, 49% of GP posts). Nurses posted most frequently about service implementation (24, 27% of nurse posts). Pharmacists posted most about health system and regulatory issues (7, 44% of pharmacist posts). CONCLUSIONS: GPs, nurses and pharmacists each have professional needs for peer support and resources to initiate or continue LARC and EMA care, with GPs in particular seeking further clinical education and upskilling. Development of resources, training and implementation support may improve LARC and EMA provision in Australian primary care.
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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 | 0.019 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".