Advanced practice nurses and their potential in home care for frail patients in rural France: A qualitative study
Bibliographic record
Abstract
CONTEXT: The ageing population in Europe, particularly in rural areas, creates new health challenges, including patients with multiple comorbidities and difficult access to care. Advanced Practice Nurses (APNs) could play a key role in improving access and care for frail older patients in France's rural areas, although how to achieve this remains unclear. AIM: This study aimed to identify the healthcare needs of frail patients living at home in rural areas and those of their healthcare providers to assess the potential role of APNs in addressing these needs. METHODS: This interpretive descriptive qualitative study was conducted from June to September 2023, using focus groups (FGs). Three FGs with 20 participants, including healthcare providers and frail older people, were conducted in two French rural areas. Data were analysed using thematic analyse to identify key needs and potential APN contributions. RESULTS: The healthcare needs identified were: improving access to care, maintaining human interactions, and providing coordinated, preventive care. Participants emphasised the importance of interprofessional collaboration and the central role of APNs, whose expanded skillset enables them to coordinate care with caregivers and professionals. However, challenges remain, including a lack of understanding of the APN's skills and concerns about their integration within the care team. CONCLUSION: APNs could support access to person-centred, coordinated home care in rural areas by acting as key references for patients, caregivers, and teams. However, limited awareness of their role and concerns from other professionals remain barriers to their integration.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".