Do All Patients Benefit From the Soothing Properties of a Conversational Nursing Intervention to Reduce Symptom Burden During Outpatient Chemotherapy?
Bibliographic record
Abstract
BACKGROUND: Soothing conversation (SC) is particularly promising for symptom management during outpatient chemotherapy. However, we know little about the profile of patients who are most likely to benefit from this intervention. OBJECTIVE: To gain a better understanding of the profile of patients most likely to benefit from SC to reduce symptom burden during outpatient chemotherapy. METHODS: We performed a multimethod secondary analysis of 2 data sets: the first gathered during a quantitative pilot trial investigating the impact of SC on patients' symptom fluctuations during chemotherapy perfusion (n = 24); the second derived from qualitative interviews about nurses' experiences with SC in this context (n = 6). RESULTS: Secondary quantitative analysis suggests that symptom control with SC is more effective in older patients, reporting lower education, widowed status, work incapacity, advanced cancer, and undergoing chemotherapy perfusion for less than 1 hour. According to nurses' interviews, SC could best benefit patients (1) prone to anxiety and fear, (2) with unalleviated pain, (3) who are unaccompanied during treatment, and contrary to what was shown with quantitative data, (4) undergoing longer perfusion duration. CONCLUSION: Although this study provides valuable insights, much work remains to be done to fully understand the factors that predispose patients to respond positively to SC during outpatient chemotherapy. IMPLICATIONS FOR PRACTICE: This study extends previous research on the effectiveness of SC for symptom management during outpatient chemotherapy by comparing nurses' experience with the intervention to patients' results. Results could be used to inform the assignment and delivery of supportive communication-based interventions during chemotherapy protocols.
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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".