The Impact of Telenursing on the Self-management of Gastrointestinal Symptoms in Adolescent Cancer Patients Receiving Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Chemotherapy is one of the cancer treatments among adolescents, after which nursing care at home is required due to developing side effects such as constipation, nausea, vomiting, and diarrhea. One solution to deliver nursing care is to provide remote self-management training. OBJECTIVE: The aim of this study is to investigate the impact of telenursing on the self-management of gastrointestinal (GI) symptoms among adolescents undergoing chemotherapy. METHODS: In this intervention study, 66 adolescents 12 to 18 years of age who were referred to teaching hospitals for receiving chemotherapy were selected through randomized block sampling. The data were collected through demographic and clinical questionnaires, the researcher-made form for GI symptoms and conditions, and the researcher-made questionnaire for the self-management of GI symptoms among adolescents. Data analysis was done using SPSS version 20. RESULTS: The findings show that there was no significant statistical difference between the control group and the intervention group in terms of demographic characteristics. According to the independent-samples t test and repeated-measures analysis of variance, using an educational website had a significant positive impact on the scores of GI symptoms self-management, 1 week and 1 month after the intervention ( P < .001). CONCLUSIONS: Given that the intervention group patients could better manage their GI symptoms on their own by visiting the educational website Cancer Information , it can be concluded that telenursing can affect the self-management of GI symptoms among adolescent patients with cancer who receive chemotherapy. IMPLICATIONS FOR PRACTICE: The website Cancerinformation.ir can be used in the self-management of GI symptoms in cancer patients.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".