Implementing advanced practice nursing in France: A country‐wide survey 2 years after its introduction
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To examine the characteristics of the first Advanced Practice Nurses in France and to compare the French model to international standards. BACKGROUND: Common barriers and facilitators to their integration in healthcare provision have been identified internationally. In France, the legislative framework was introduced in 2016, and the first graduates entered the workforce in 2019. METHODS: The French model was examined in comparison with Hamric's conceptual framework and to the International Council of Nurses' guidelines and definitions. A cross-sectional survey was also conducted, using three self-administered online questionnaires. Two were distributed to 2019 and 2020 graduates and a third to the accredited programme directors. The characteristics of advanced practice nursing graduates were described and compared based on employment status and field of practice (primary vs secondary/tertiary care). RESULTS: Although the French model of advanced practice nursing meets Hamric's primary criteria and core competencies, it does not differentiate between Nurse Practitioner and Clinical Nurse Specialist roles. Of the 320 students enrolled in one of the 11 accredited training programmes 165 participated in the survey. Mean age was 40, and mean prior nursing experience was 15 years. By February 2021, 30% of respondents were still employed as Registered Nurses. Barriers to practice included insufficient income generation (primary care), the lack of position creation (secondary/tertiary care), the physician-dependent patient referral process and delays in prescription credentials approval. CONCLUSIONS: The implementation of advanced practice nursing in France faces several barriers. Legislative adjustments and greater financial incentives to practice seem warranted. RELEVANCE TO CLINICAL PRACTICE: as in other countries, France introduced advanced practice nursing to respond to the Public Health challenge of improving access to quality health care in the context of increasing chronic disease prevalence and limited resource allocation. Facilitating its integration in the healthcare provision landscape seems paramount.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it