The nurse practitioner role in pain management in long‐term care
Bibliographic record
Abstract
AIM: This paper is a report of a study exploring the perceptions of long-term care team members and nurse managers about barriers and facilitators to optimal use of nurse practitioners to manage residents' pain in long-term care settings. BACKGROUND: Considering the high rates of pain in long-term care, research is needed to explore innovations in health-services delivery, including the emerging nurse practitioner role. METHODS: For this study, an exploratory descriptive design was used to collect data in spring 2007 from five focus groups of nurses and 14 individual interviews with other healthcare team members and nurse managers. Data were analysed using thematic content analysis. FINDINGS: Five pain management activities performed by nurse practitioners were identified, including assessing pain, prescribing pain medications, monitoring pain levels and side effects of pain medications, consulting and advocating for staff and patients, and leading and educating staff related to pain management. Factors that influenced the implementation of the nurse practitioner role included the availability of the nurse practitioner, scope of practice, role clarity, perceived added value of nurse practitioner role, terms of employment, nurse practitioner-physician relationship. Perceived outcomes of the nurse practitioner role were also described. CONCLUSIONS: The findings from this study contribute to our understanding of how the nurse practitioner role is perceived by other healthcare professionals, particularly in pain management. Stronger interprofessional collaborative relationships need to be facilitated within a model of care that includes a nurse practitioner, with the ultimate goal of improving pain management services in long-term care.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".