Investigating the effects of watson-based education on depressive symptoms, hope, and pain in cancer patients undergoing chemotherapy: a randomized controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Cancer is among the most important chronic illnesses and one of the main public health concerns worldwide, which has significant physical, social, psychological, and spiritual consequences for the patient and his family. This disease reduces hope in patients and leads to depressive symptoms as well as pain. This research was designed and performed to explore the impact of teaching according to Watson's theory on depressive symptoms, hope, and pain in patients suffering from cancer undergoing chemotherapy in southern Iran. METHODS: This is a randomized controlled trial research without blinding. A total of 120 randomly selected patients were assigned to two intervention groups (n = 60) and a control group (n = 60). The intervention underwent education according to Watson's theory. Data were collected using the Beck Depression Inventory (BDI), Miller Hope Scale (MHS), and McGill Pain Questionnaire (MPQ). Data analysis was performed using SPSS version 22. To analyze the data, we used descriptive statistics. Accordingly, inferential statistics applied included chi-square, independent-samples t-test, and repeated measures (ANOVA). We also used analysis of covariance (ANCOVA) to determine whether there are any significant differences between the two groups. Significance level was considered as p < 0.05. RESULTS: Mean scores for depressive symptoms, hope, and pain intensity, measured two weeks and two months after the intervention, were significantly higher in the intervention group (p < 0.05). However, for the control group, the difference was not statistically significant. CONCLUSION: Education based on Watson's theory positively improved hope and reduced depressive symptoms as well as pain intensity in cancer patients. Nursing managers and health system policy makers can use this educational approach for people with other chronic conditions. IRANIAN REGISTRY OF CLINICAL TRIALS: IRCT registration number: IRCT20190917044802N6. REGISTRATION DATE: 31/7/2022.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".