Assessment of Guided Imagery Effect on Reducing Anxiety and Pain Associated with Wound Dressing Changes in Burn Patients
Bibliographic record
Abstract
Background: Burn damages are important causes of mortality and morbidity. They are also associated with many physical, psychological, social, and economic consequences. Objectives: The present study was conducted to assess the effect of guided imagery on reducing anxiety and pain due to dressing change in burn patients. Methods: The statistical population of this clinical trial included all burn patients (grade 1 and 2 but not self-immolation) who admitted to the burn department of Imam Reza hospital in Mashhad (the second largest city in Iran) between September 2012 and March 2013. 40 patients selected non-randomly through convenience sampling method were divided randomly into two equal groups of intervention and control. The intervention group received guided imagery treatment (15 minutes per day for 8 days) in addition to the routine care, while the control group only received the routine care. Data were gathered through a demographic form, Beck anxiety inventory, and McGill pain inventory. The data were analyzed by descriptive statistics such as frequency, mean, and standard deviation and inferential statistics such as independent t-test in SPSS software. Results: The comparison of anxiety and pain between the two groups in pre-test showed no significant differences (P = 0.310 and P = 0.120, respectively). However, there were significant differences in the scores of anxiety and pain between the groups in post-test (P = 0.001 and P = 0.001, respectively). Conclusions: It seems that guided imagery can reduce the level of anxiety and pain due to dressing change in burn patients.
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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 | 0.001 |
| 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) | 0.002 | 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".