Adaptation to illness in relation to pain perceived by patients after surgery
Bibliographic record
Abstract
Background: Pain is one of the factors that decrease quality of life. Undergoing surgery is inevitably associated with the sensation of pain, which can affect a patient’s level of acceptance of an illness. The aim of the study was to evaluate the level of acceptance of illness in patients undergoing surgical treatment with relation to the pain perceived by them during surgical treatment and to determine other factors that affect adaptation to illness among patients subjected to invasive treatment. Material and methods: The study was conducted on a group of 100 patients with mean age of 51.27 (SD=18.98) hospitalized in surgery departments in the Provincial Specialist Hospital in Wrocław, Poland, in April 2016. The Acceptance of Illness Scale (AIS) and the Visual Analog Scale (VAS) for pain were used. Results: The mean score of VAS was 3.86 (SD =2.02). The mean score of AIS was 24.42 (SD =7.35). The level of acceptance of illness was significantly negatively correlated with the intensity of pain ( p <0.001; r =−0.498), the number of coexisting diseases ( p =0.002; r =−0.31), age ( p <0.001; r =−0.391), and the period of time since the operation ( p =0.007; r =−0.266). Patients taking analgesics showed a significantly lower acceptance of illness than those who did not ( p =0.009). A patient’s place of living, education, and sex had no significant impact on their acceptance of illness. Conclusion: A higher level of pain translates into a lower adaptation to illness despite the use of analgesics, which may indicate that inadequate pain control leads to a decrease in the acceptance of illness. Further research on monitoring postoperative pain, as well as the development of postoperative prevention programs, is required. Keywords: pain, acceptance of illness, surgical treatment, postoperative pain
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".