A Multidisciplinary Preoperative Teaching Session for Women Awaiting Breast Cancer Surgery: A Quality Improvement Initiative
Bibliographic record
Abstract
Purpose: Most of the breast cancer diagnoses are recommended for breast surgery. Unfortunately, many patients report preoperative anxiety, which can affect postoperative recovery. Preoperative teaching sessions have been shown to reduce anxiety and improve recovery for the patients with breast cancer. To better support the patients at our cancer center, a multidisciplinary preoperative teaching session was developed and delivered as a quality improvement initiative. Methods: Participants scheduled for breast surgery were invited to attend a group-delivered preoperative teaching session, either for breast-conserving surgery or mastectomy. The sessions were presented by a nurse, occupational therapist, and physiotherapist. Data were collected through a researcher-developed 2-item questionnaire administered before and after sessions to compare self-reported anxiety and knowledge levels, along with qualitative feedback. Results: A total of 94 participants attended the preoperative sessions, piloted over a year. The majority were scheduled for breast-conserving surgery. Wilcoxon signed rank tests showed that after session, self-reported levels of anxiety decreased, whereas levels of knowledge increased. Most participants found the session to be very helpful and would recommend it to other patients/families awaiting surgery. Conclusions: Patients awaiting surgery for breast cancer may be better supported through a multidisciplinary group teaching session by decreasing anxiety and improving knowledge related to the procedure. Future directions could explore the effect of specific session elements on anxiety, knowledge, and postoperative complications using psychometrically sound instruments and additional time points. Implications for cancer survivors: Standardization of these preoperative teaching sessions may enhance breast cancer care, reduce postoperative complications, and improve patient recovery.
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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 | 0.002 | 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) | 0.001 | 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".