The effects of quantum psychological relaxation technique on self-acceptance in patients with breast cancer
Bibliographic record
Abstract
BACKGROUND: Psychological, as well as physical effects of a disease, and side effects of medications can influence the self-acceptance of patients. Low self-acceptance can lead patients to drop out of their therapy schedule and return to the hospital several months later in much worse condition. This study aimed to investigate the effects of quantum psychological relaxation technique on self-acceptance in breast cancer patients. METHODS: This study used a pre-post quasi-experimental design with a control group. The sample included 64 respondents selected using a consecutive sampling technique and divided into two groups: intervention group (n=32) and control group (n=32). The quantum psychological relaxation technique was administered in two phases with three sessions for each phase. Six sessions were estimated at 90-120 minutes and 15-20 minutes for each. The data on patients' self-acceptance were collected using the Acceptance of Illness Scale (AIS) questionnaire and were statistically analyzed using t-test and Wilcoxon test. RESULTS: Self-acceptance in the intervention group increased after being given the intervention with a p-value <0.001. However, in the control group there was no significant increase in self-acceptance with a p-value >0.005. CONCLUSIONS: The quantum psychological relaxation technique had an effect on the self-acceptance of breast cancer patients. RECOMMENDATION: Further studies of the effects of quantum psychological relaxation technique on depression and life quality of patients need to be conducted.
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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 | 0.001 | 0.003 |
| 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".