Nurses' pain assessment practices with critically ill adult patients
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".