An Interpretive Description Exploring How Nursing Faculty Teach Pain Assessment and Management in the Clinical Setting
Bibliographic record
Abstract
Background Nurses require competencies in pain assessment and management to provide safe and compassionate care, yet gaps persist in how effectively undergraduate education prepares students for this aspect of practice. There is limited evidence on how clinical nursing faculty teach pain in practice settings or the factors influencing their teaching. Purpose The purpose of my study was to explore how nursing faculty teach pain assessment and management in clinical settings. I examined faculty knowledge, attitudes, and teaching strategies, as well as the influence of clinical and curricular contexts on teaching and learning. I also explored how these elements influenced faculty’s approach to supporting student learning and ensuring that students are prepared to manage pain effectively in practice. Methods I used an interpretive description design with mixed methods. Twenty-three faculty from undergraduate nursing programs in western Canada completed the Knowledge and Attitudes Survey Regarding Pain (KASRP). Descriptive statistics identified strengths and gaps in knowledge and attitudes. Fourteen faculty participated in individual interviews and four in a focus group. Transcripts were analyzed using constant comparative analysis. Findings I identified three themes from my analysis: (1) Internal factors influencing teaching included faculty knowledge, confidence, and prior experiences, which shaped how they engaged students in learning. Faculty had strong foundational knowledge but identified gaps in opioid use, dependency, and cancer pain management. The faculty’s confidence in their teaching role or comfort in the clinical environment influenced whether they focused more on tasks or facilitated reflective, person-centred learning. (2) Contextual factors shaping teaching and learning included unit culture, staff attitudes, and student beliefs. Supportive staff created positive role modelling opportunities, while workload pressures, stigma, and uncertainty about opioid use sometimes limited student learning. Faculty’s knowledge of where pain curriculum was taught in the program influenced clinical teaching, as unclear expectations and inconsistent communication between theory and clinical courses left some faculty unsure of what content to emphasize. (3) Preserving the ideal in practice-based learning described how faculty promoted person-centred care and professional identity development related to managing pain. Strategies included using real patient encounters, reflection, debriefing, storytelling, and advocacy to help students integrate theoretical concepts, develop empathy, and strengthen clinical reasoning. Implications My findings highlight opportunities to strengthen pain education in nursing and improve the delivery of pain care. Clinical faculty play a vital role in shaping students’ knowledge, attitudes, and advocacy skills through patient encounters, reflective dialogue, and role modelling of ethical practice. Nursing programs can enhance education by investing in faculty development, clarifying curriculum expectations, and addressing the powerful influence of unit culture on student learning. Supportive clinical environments enable faculty to reinforce compassionate, evidence-informed pain care, while negative norms and stigma create barriers to both learning and patient outcomes. Practical strategies include designating pain education champions, creating accessible teaching resources, and promoting interdisciplinary collaboration to foster consistent, person-centred practices. Future research should evaluate how these strategies, along with initiatives to strengthen unit culture, influence student competencies, faculty teaching practices, and patient outcomes across healthcare settings. Conclusion Faculty play a central role in preparing nursing students to assess and manage pain. Their knowledge, confidence, and teaching approaches, combined with the curriculum structure and clinical context, influence how students learn and apply concepts related to pain. Strengthening faculty preparation, clarifying curriculum expectations, and fostering supportive clinical environments can create consistent learning opportunities and help students deliver ethical, person-centred pain care. Continued research evaluating clinical teaching strategies and faculty development will further advance pain education in nursing.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".