Nurses Journey of Postoperative Pediatric Pain Care: A Qualitative Study Using Participation Observation in Indonesia
Bibliographic record
Abstract
BACKGROUND: Unrelieved post-operative pain in children continues to be a major clinical problem, despite advances in pain management in Indonesia. The significance of the study is to address the gap in nurses’ knowledge of pain management may be having. The study aim was to examine nurses post-operative pain care in an Indonesian Hospital. METHODS: A naturalistic, observational qualitative approach was undertaken to observe16 participating nurses who cared for 16 children post-surgery. Each participant was observed continuously during three to four shifts of 5 h each over a 2-month period. Content analysis was performed to interpret the results. RESULTS: We found that, in general, the nurses did not routinely and comprehensively assess the extent to which the children were in pain post-surgery and that they rarely used non-pharmacological interventions. Such these interventions were often conducted by parents. However, the nurses readily provided analgesic drugs as needed to the children, especially during the first 48 h post-operative period. Our findings support those of previous studies that found the role of nurses in pain management is primarily administration of analgesic drugs. Moreover, such pain care did not conform to recommendations based on current evidence. CONCLUSIONS: Post-operative pain care by nurses in a pediatric surgical ward were still un-optimal. These findings increase our knowledge and understanding about the complexities of postoperative pain care of children in Indonesia. Post-operative pain management in pediatric patients could be improved by increasing cooperation among healthcare professionals and parents. Post-operative pain management should be always put as a priority
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".