Nurses' pain assessment practices with critically ill adult patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: This study aimed to describe the perceived barriers, enablers and acute pain assessment practices of nurses caring for critically ill adult patients in a resource-limited setting. BACKGROUND: Acute pain is a common problem among critically ill adult patients, and nurses' play a central role in its control. Very few studies have examined nurses' acute pain assessment practices in resource-limited settings. METHODS: A descriptive and cross-sectional design was used. A total of 170 nurses working in a Ugandan hospital were enrolled. Data were collected using a questionnaire measuring various aspects of pain assessment for critically ill adult patients. RESULTS: The majority of nurses had poor pain assessment practices. The most commonly performed pain assessment practices were documenting assessment findings, discussing pain assessment and management during nurse-to-nurse reports, and assessing for analgesics need before wound care. The main barriers to pain assessment were workload; lack of education and familiarity with assessment tools; poor documentation and communication of pain assessment priorities. The only reported enabler was physician's prescriptions for analgesia. Pain assessment practices were significantly associated with perceived workload and priority given to pain assessment. CONCLUSION: Pain assessment practices of nurses caring for critically ill adult patients in a resource-limited setting are affected by several barriers. IMPLICATION FOR NURSING AND HEALTH POLICY: Interventions to reduce barriers and enhance enablers of acute pain assessment are needed to improve pain management in critically ill adult patients. To be effective, the interventions have to be holistic and implemented by professional bodies and employers of nurses.
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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 | 0.006 |
| 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 | 0.000 |
| 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 it