A mixed-method study evaluating an innovative care model for rural patients undergoing outpatient breast surgery
Bibliographic record
Abstract
BACKGROUND: The Delta Oasis program was launched in New Brunswick in 2006 to offer patients from rural areas who were undergoing breast cancer surgery and their families 1 night of free accommodations and a postoperative consultation with an extramural nurse. We sought to investigate patient experiences with this program. METHODS: This mixed-method retrospective study took place from 2020 to 2022 and compared the preoperative anxiety and quality of recovery of program participants and control patients who were discharged home over 100 km from hospital. We conducted 2 × 2 analysis of variance to evaluate the effects of intervention group and surgery type. We conducted semistructured interviews with intervention participants, which we then thematically analyzed. Two patient partners were engaged during data synthesis to support the interpretation of results. RESULTS: We included 34 patients who participated in the program and 18 control patients. No statistically significant differences were found between treatment groups in preoperative anxiety and quality of recovery, regardless of surgery type. Thematic analysis of interviews with 17 intervention participants revealed that they were highly satisfied with the program and that the experience helped reduce stress and discomfort related to their surgery. INTERPRETATION: The Delta Oasis program is a cost-effective alternative to inpatient care after breast cancer surgery and is highly regarded by rural patients; expansion to other regions with the inclusion of additional low-risk surgeries could help address hospital capacity issues. This study contributes to our understanding of the patient experience with the Delta Oasis program and informs the development of similar programs elsewhere.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".