Evaluation of the Nurse-Led Symptom Management Program for Patients With Gynecologic Cancer Undergoing Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Patients with cancer experience symptoms concurrently. Nurses need to make multisymptom management and educate patients about self-management strategies. OBJECTIVE: The aim of this study was to evaluate the effect of a nurse-led symptom management program (NL-SMP), developed based on the Symptom Management Model, quality of life (QoL), and symptom severity of women with gynecological cancer undergoing chemotherapy. METHODS: This randomized controlled study sample consisted of 41 women receiving chemotherapy at an outpatient clinic in Istanbul, Turkey, between November 2018 and December 2019. European Organisation for Research and Treatment of Cancer Quality-of-Life Scale, Edmonton Symptom Assessment Scale, and Modified Brief Sexual Symptom Checklist-Women were used to collect data. Women were randomly assigned to 2 groups: intervention (n = 21) and control (n = 20). The intervention group attended the NL-SMP in addition to usual care. Data were collected at the first (time 1), third (time 2), and last chemotherapy cycle (time 3). Repeated measures analysis of variance, Cochran-Q, and t tests were used to analyze the data. RESULTS: In the intervention group, the QoL was significantly higher; symptom severity was lower than that of the control group at time 2 and time 3. At time 3, more women in the control group reported at least 1 sexual difficulty and were not satisfied with their sexual function, whereas there was no change for women in the intervention group. CONCLUSION: The NL-SMP, which consisted of systematic symptom assessment, prioritization of symptoms, providing symptom, and patient-specific education, decreased deterioration in the QoL and symptom severity of women. IMPLICATIONS FOR PRACTICE: Conducting multisymptom assessments, prioritizing symptoms, providing symptom- and patient-specific education, and supporting symptom self-management throughout treatment can lead to effective symptom management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".