Co‐design of a nurse‐led model of care to increase access to medical abortion and contraception in rural and regional general practice: A protocol
Bibliographic record
Abstract
PROBLEM: Women in rural and regional Australia experience a number of barriers to accessing sexual and reproductive health care including lack of local services, high costs and misinformation. SETTING: Nurse-led task-sharing models of care for provision of long-acting reversible contraception (LARC) and early medical abortion (EMA) are one strategy to reduce barriers and improve access to services but have yet to be developed in general practice. KEY MEASURES FOR IMPROVEMENT: Through a co-design process, we will develop a nurse-led model of care for LARC and EMA provision that can be delivered through face-to-face consultations or via telehealth in rural general practice in Australia. STRATEGIES FOR CHANGE: A co-design workshop, involving consumers, health professionals (particularly General Practitioners (GPs) and Practice Nurses (PNs)), GP managers and key stakeholders will be conducted to design nurse-led models of care for LARC and EMA including implant insertion by nurses. The workshop will be informed by the 'Experience-Based Co-Design' toolkit and involves participants mapping the patient journey for service provision to inform a new model of care. EFFECTS OF CHANGE: Recommendations from the workshop will inform a nurse-led model of care for LARC and EMA provision in rural general practice. The model will provide practical guidance for the set-up and delivery of services. LESSONS LEARNT: Nurses will work to their full scope of practice to increase accessibility of EMA and LARC in rural Australia.
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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 | 0.072 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".