A nurse‐led model of care to improve access to contraception and abortion in rural general practice: Co‐design with consumers and providers
Bibliographic record
Abstract
AIM: To describe key features of a co-designed nurse-led model of care intended to improve access to early medication abortion and long-acting reversible contraception in rural Australian general practice. DESIGN: Co-design methodology informed by the Experience-Based Co-Design Framework. METHODS: Consumers, nurses, physicians and key women's health stakeholders participated in a co-design workshop focused on the patient journey in seeking contraception or abortion care. Data generated at the workshop were analysed using Braun and Clarkes' six-step process for thematic analysis. RESULTS: Fifty-two participants took part in the co-design workshop. Key recommendations regarding setting up the model included: raising awareness of the early medication abortion and contraceptive implant services, providing flexible booking options, ensuring appointment availability, providing training for reception staff and fostering good relationships with relevant local services. Recommendations for implementing the model were also identified, including the provision of accessible information, patient-approved communication processes that ensure privacy and safety, establishing roles and responsibilities, supporting consumer autonomy and having clear pathways for referrals and complications. CONCLUSION: Our approach to experience-based co-design ensured that consumer experiences, values and priorities, together with practitioner insights, were central to the development of a nurse-led model of care. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The co-designed nurse-led model of care for contraception and medication abortion is one strategy to increase access to these essential reproductive health services, particularly in rural areas, while providing an opportunity for nurses to work to their full scope of practice. IMPACT: Nurse-led care has gained global recognition as an effective strategy to promote equitable access to sexual and reproductive healthcare. Still, nurse-led contraception and abortion have yet to be implemented andevaluated in Australian general practice. This study will inform the model of care to be implemented and evaluated as part of the ORIENT trial to be completed in 2025. REPORTING METHOD: Reported in line with the Standards for Reporting Qualitative Research (SRQR) checklist. PATIENT OR PUBLIC CONTRIBUTION: Two consumer representatives contributed to the development of the co-design methodology as members of the ORIENT Intervention Advisory Group Governance Committee.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".