BARRIERS TO THE IMPLEMENTATION OF THE ADVANCED PRACTICE NURSING ROLE IN PRIMARY HEALTH CARE SETTINGS: AN INTEGRATIVE REVIEW
Bibliographic record
Abstract
Background: Advanced practice nurses are nurses prepared with advanced clinical education, skills, and competencies required to assess, diagnose, treat and deliver continuous care for acute or chronic conditions.The move toward using advanced practice nurses in primary healthcare settings in Qatar is inevitable to advance the nurse's role, improve the level of services provided, raise patient satisfaction, and improve the organizational outcomes.Aim: The aim of this review was to explore the barriers in implementing advanced practice nursing in primary health care settings in order to facilitate its implementation in Qatar.Method: Whittemore and Knalf's framework guided this integrative review.Fourteen studies published between 2009 and 2019 were included in the review.The mixed-methods appraisal tool was used to assess the quality of the studies.The socio-ecological model was used to categorize and present barriers at the individual; organizational, social, cultural, policies, and environmental level.Result: Three main barriers noted were a lack of clarity and support of the role, lack of organizational and policy support for the role, and a lack of designated space for APN practice.Conclusion: Identifying and addressing barriers is necessary to achieve successful implementation of the APN role within primary healthcare in Qatar.Key recommendations for Qatar include integrating key stakeholders in the implementation process, use of a clear job description and policies, and providing designated workspaces for APN practice.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".