Pain experience, pain management strategies and satisfaction of hospitalized trauma patients in Indonesia
Bibliographic record
Abstract
Although trauma is a common cause of greater pain and interference on daily activities, little is known about pain experience, pain management strategies and pain management outcomes in hospitalized trauma patients in Indonesia. This descriptive study aimed (1) to assess the pain experience, (2) to describe pain management strategies, and (3) to describe satisfaction with pain management conducted by healthcare providers as perceived by trauma patients. A total of 154 hospitalized trauma patients from a teaching hospital in Indonesia were recruited from January to March 2016. Data were analyzed using descriptive and inferential statistics. The study found that most of the hospitalized trauma patients had single extremity fractures (56.49%) and mild head injury (20.13%). They have experienced a mild to moderate level of pain intensity and pain interference during the first three days of admission. These pain intensity and pain interference levels were found to be significantly decreased from the first to the third day. The pain management strategies often used by the healthcare providers were showing interest and asking about pain, assessing the outcomes after receiving analgesic drugs, and giving information about pain. The pain management strategies often used by patients were praying (86.36%), slow and deep breathing (77.27%), and reciting Dzikir (meditation) (68.18%). Patients reported that performing Dzikir and praying were the effective strategies to reduce their pain. The patients rated moderate to high levels of satisfaction with pain management conducted by healthcare providers. Therefore, combinations analgesic drugs with praying and performing Dzikir related to cultural contexts are crucial to alleviate pain among hospitalized trauma patients in Indonesia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".