Cancer educational group intervention: impact on self-efficacy and anxiety among women recently diagnosed with breast cancer and undergoing surgery
Bibliographic record
Abstract
Aim: To investigate the impact of an educational group intervention on self-efficacy and anxiety among women recently diagnosed with breast cancer.Method: A quasi-experimental longitudinal design was used. Women diagnosed with breast cancer and undergoing surgery (n = 113) were recruited from a university teaching hospital in Montreal, Quebec, Canada. At pre-admission, the intervention group(n = 57) received a group 90-minute information session (paper documents, video and demonstration) led by a nurse and a physiotherapist, which included material handling and time for questions and discussion. The control group(n = 56) received the usual care. Self-reported questionnaires were filled out at the time of the announcement of surgery(T0), after the training(intervention or usual care, T1) and at the first post-operative follow-up(T2).Results: Quantitative analysis using a mixed model for repeated measures showed no significant differences between the experimental and control groups in terms of the training session (group vs. individual). A time effect was observed in both groups for self-care and anxiety. In addition, several positive changes weremade in the clinical settings to optimize oncology healthcare.Conclusions: Future research would explore whether these findings reflect actual clinical practices or are more dependent on the specific cancer diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".