Student Nurses’ Experiences and Reflections on Pain Management in the Clinical Setting: An Exploratory Analysis of Students’ Choice of Assignment Topic
Bibliographic record
Abstract
Background Pain, particularly chronic pain, represents a global health burden. The provision of undergraduate pain education for health professionals remains suboptimal, and yet pain features as an important competency for successful licensure in Canada. Purpose To explore what clinical events undergraduate nursing students identify as critical to their learning. If pain featured, then to describe the nature of the pain incident. Methods A retrospective cross-sectional design with a thematic analysis of year 3 undergraduate student nurses’ assignments was used. For the assignments identified as related to pain, a more detailed inductive content analysis was used to provide a condensed but broad description of the data. Results A total of 215 students participated. The most reported topics were pain (14.8%), patient assessment (10.2%), patient-/family-centered care (10.2%), and effective communication (9.8%). For those who described a pain encounter in their clinical experience, advocacy, managing the gap, and a lack of knowledge were the main focus. Conclusions This study provided valuable insights to the ways in which student nurses wrote about their experiences and management of pain in the clinical setting. Strengthening learning in the nursing curricula around advocacy and conflict management might provide new ways to improve pain education.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".