The Effect of the Neuman Systems Model–Based Training and Follow-up on Self-Efficacy and Symptom Control in Patients Undergoing Chemotherapy
Bibliographic record
Abstract
In this article, the authors aimed to determine the effect of the training and follow-up based on the Neuman systems model provided to patients undergoing chemotherapy on their self-efficacy and symptom control. The study was carried out with a randomized controlled experimental study model design. The sample consisted of 102 patients including 52 in the experimental group and 50 in the control group. The data were collected using the Patient Information Form, the Cancer Behavior Inventory–Brief (CBI-B), and the Edmonton Symptom Assessment Scale (ESAS). A personal training program prepared according to the Neuman systems model was applied to the experimental group patients. In the intergroup comparison of the experimental and control group patients, there was an increase in the posttest CBI-B scores and a decrease in the ESAS scores in the experimental group compared to the control group, and the intergroup difference was statistically significant ( p < .05). According to the results, to improve the self-efficacy and symptom control in patients undergoing chemotherapy, using this education and follow-up program is recommended.
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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".