Evaluating the Effect of Slow-Stroke Back Massage on the Anxiety of Candidates for Cataract Surgery
Bibliographic record
Abstract
Background: The patients under cataract sur-gery often experience anxiety not only during the surgery, but also prior to the surgery.Purpose: We sought to determine the effects of slow-stroke back massage on anxiety in patients undergoing cataract surgery. Setting: The study was conducted in the Amiral-momenin Hospital of Zabol city, south-east of Iran.Participants: A total of 60 candidates of cataract surgery participated in the study.Research Design: The participants were ran-domly allocated to either control or intervention groups. The intervention group received slow-stroke back massages, while patients in control group received routine interventions.Intervention: The slow-stroke back massage was performed on the patients assigned to the interven-tion group. The intervention was performed in the morning of the surgery day at 30 minutes before the surgery. The researcher performed each mas-sage session in a sitting position. The duration of each massage session was 15 minutes. Main Outcome Measures: Anxiety was assessed in the both groups in the morning of the surgery, before and immediately after the intervention. In-dependent samples Student’s t test, paired samples Student’s t test, and chi-squared test were used to analyze the data.Results: Anxiety was not significantly different between the two groups before and after the mas-sage (p = .816). On the other hand, paired samples Student’s t test showed a significant difference comparing the anxiety scores before (49.7±5.43) and after (45.16±3.89) the massage in the interven-tion group (p < .001). Conclusions: Based on our results, slow-stroke back massage, which is a low-cost and safe method, reduced anxiety in patients who were candidates for cataract surgery.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".