Unleashing the Potential of Primary Care Nurses in Chronic Pain Management: A Delphi Study to Identify Priority Activities
Bibliographic record
Abstract
Chronic pain (CP) is a major cause of global disability, with patients often reporting inadequate access to primary care. Relevant primary care nursing activities for CP management are poorly defined, limiting the effective use of nurses' competencies in managing chronic diseases. This study aimed to identify and prioritize nursing activities for CP management. A three-round Delphi study was conducted among primary care nurses and individuals experiencing pain for over 3 months in Québec, Canada. In the first round, participants identified important nursing activities for CP management through open-ended questions. In the following two rounds, these activities were rated for importance using a 9-point Likert scale. Activities rated 7, 8, or 9 by ≥75% of participants were considered priority activities. A total of 48 nurses and 122 patients participated (n = 170) in the process. From 47 nursing activities derived from 1,167 narrative suggestions in the first round, 41 were prioritized by ≥75% of participants in the final round. These activities were categorized into four domains: global assessment (n = 15, 36.6% of all activities), care management (n = 10, 24.4%), health promotion (n = 7, 17.1%), and interprofessional collaboration (n = 9, 22.0%). The top three activities were: assessing dimensions of pain experience, screening for mood disorder symptoms, and establishing a therapeutic alliance with an empathic approach. Nurses and persons with lived experience identified a shared set of nursing activities for management of CP, aligning with usual care practices for chronic disease patients. These findings could inform clinical guidance on CP management and enhance the role of nurses in primary care settings.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".