A Cross-Sectional Analysis of Ambulatory Oncology Experience by Treatment Intent
Bibliographic record
Abstract
The Ambulatory Oncology Patient Satisfaction Survey (AOPSS) is a standardized instrument to assess the overall cancer patient experience. This study retrospectively investigated differences in care experiences and satisfaction among ambulatory oncology patients who self-identified as receiving outpatient therapies for curative intent or for symptom or disease control. This cross-sectional study analyzed data from the AOPSS collected between February and April 2019 within the provincial cancer program in Alberta, Canada. There were 2104 participants who returned the survey, representing a 52.7% response rate. This nationally validated survey gathers patient care experiences and satisfaction across six domains of person-centred care. Treatment intent was characterized by adding a new "goal of treatment" question. Statistical analysis was performed using Mann-Whitney U tests and analysis of covariance (ANCOVAs). Cancer patients' treatment goals were found to be significantly associated with key patient characteristics like age, sex, tumour group, and the locations where they received care. Patients whose self-identified goal of treatment was to cure their cancer reported significantly higher levels of satisfaction and a more positive experience in five out of the six person-centred care domains. Results identify marked differences in satisfaction and experience between these two patient groups even though they both received care in the same ambulatory environments. A better understanding of the experience and satisfaction of non-curative cancer patients could allow for a more holistic and supportive approach to patient care. In addition, an early palliative approach to care is recommended for improved patient outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".