Day Surgery for Breast Cancer: Effects of a Psychoeducational Telephone Intervention on Functional Status and Emotional Distress
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To determine the efficacy of a nursing intervention based on self-regulation theory known as the Attentional Focus and Symptom Management Intervention (AFSMI) in enhancing physical and emotional well-being in women who underwent day surgery for breast cancer. DESIGN: Randomized clinical block trial; subjects were randomly allocated to the experimental group (n = 61) or the usual care (control) group (n = 56). Subjects in the experimental group received the AFSMI during two phone sessions, at 3-4 days and 10-11 days after surgery. SETTING: The convenience sample was drawn from five regional centers located in different geographic areas (urban and rural regions) in Quebec, Canada. SAMPLE: 117 patients with primary breast cancer who underwent day surgery as part of their initial treatment for cancer. METHODS: Data collection and nursing intervention via telephone interviews. MAIN RESEARCH VARIABLES: Functional status and emotional distress. FINDINGS: Significant differences between the experimental and control group were found at post-test on home management, total mood disturbance, confusion, and tension scores. CONCLUSIONS: The AFSMI was effective in reducing emotional distress and enhancing physical functioning. IMPLICATIONS FOR NURSING: Findings validate the use of the self-regulation model in designing individualized nursing interventions. Redirecting attention and focusing on concrete objective features hold potential in developing other innovative nursing interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".