Factors influencing pain management by nurses in emergency departments in Central Africa
Bibliographic record
Abstract
OBJECTIVE: To observe pain management practices by nurses in emergency departments (EDs) in Central Africa and to study the various factors influencing these practices. METHODS: Time to first analgesic treatment was recorded in 53 patients presenting to the ED of a Central African hospital in February 2005. A survey was simultaneously conducted on the attitudes and commitment of nurses towards the management of pain. All 28 nurses assigned to the ED agreed to participate in the survey. RESULTS: Severity of pain was the factor most influencing the time to first analgesia following admission to the ED. Severe pain was assessed as a score of > or = 7 on a 1-10 visual analogue scale. The median time to first analgesia in patients with severe pain was 150 min, which was considerably longer than in patients without severe pain (p = 0.003). A quarter of the 28 nurses had no official training in pain management and most (> 80%) were unable to carry out a formal assessment of pain. The majority (> 90%) were confident of their ability to treat pain. Thirteen (48%) were of the opinion that cultural factors influenced their management of pain and 67% admitted that they had some fears about administering morphine to patients in the ED. CONCLUSION: Pain management by nurses in the ED in Central Africa is inadequate. Cultural factors greatly influence how nurses manage pain in the emergency room. Patients would benefit considerably if nurses received additional education about the diagnosis and management of acute pain in EDs in Central Africa.
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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 | 0.001 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".