The Patient Experience of Ambulatory Cancer Treatment: A Descriptive Study
Bibliographic record
Abstract
Background: Patient experience is often measured quantitatively, but that approach has limitations for understanding the entire experience. Qualitative methods can help to understand more complex issues most important to patients and their families. The purpose of the present work was to use a qualitative analysis examining the patient experience of ambulatory cancer care in Ontario to generate a deeper understanding of the patient experience and to lead to solutions for improvement. Methods: Data from the Ambulatory Oncology Patient Satisfaction Survey (aopss) for 2013-2015 were used to conduct a qualitative content analysis. The aopss is a retrospective paper-based survey, mailed to patients who are currently receiving cancer treatment or who have received cancer treatment within the preceding 6 months, that is designed to capture their experiences. Patients who were surveyed were asked, "Is there anything else you would like to tell us about your cancer care services?" The National Research Corporation Canada's patient-centred care framework was used to guide the analysis. Results: From the 5391 patients who responded, 7328 coded responses were generated, of which 3658 (49.9%) were related to the patient-centred care framework. New subthemes were identified: diagnosis sensitivity; emotional support resources; care delivery with care, compassion, and comfort; continuity of care between departments and in the community; access to cancer centre personnel; patient-health care provider communication; confidence in the health care provider; wait times; health care provider and treatment coordination; and parking. Conclusions: The results identify facilitators and barriers to the patient experience in the ambulatory cancer treatment setting from the patient perspective and identify opportunities to improve the patient experience.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".