An Exploration of the Unique Factors Contributing to NP Burnout
Bibliographic record
Abstract
Aim This study aimed to explore unique factors contributing to nurse practitioner (NP) burnout. Background Burnout is a significant issue affecting healthcare professionals, including nurse practitioners, which can lead to negative outcomes like decreased quality of care, reduced job satisfaction, and increased intent to leave the profession. While common burnout factors like high workload and dysfunctional team dynamics are well-documented, nurse practitioners may face unique challenges that further exacerbate these issues. Methods A qualitative descriptive approach was used, involving four focus groups with 23 NPs from four Canadian provinces. Transcripts from the focus groups were analyzed through content analysis to identify recurring themes. Findings The analysis revealed that, in addition to common burnout factors, NPs experience unique challenges. Key themes that emerged included Undervalued Professional Worth, Lack of Autonomy, and Organizational and Systems Pressures. Conclusion The issues identified in this study underscore the need for strategies aimed at mitigating NP burnout. Addressing these unique challenges could improve job satisfaction, enhance the quality of care, and reduce NPs’ intent to leave the profession
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".