Care Coordination and Patient Satisfaction With Ambulatory Cancer Care During the COVID-19 Pandemic in Manitoba, Canada: Report of An Online Survey Study of Patient-Reported Experience Measures With Interpretation Guided by Fit Theory
Bibliographic record
Abstract
Background: In Manitoba, Canada, the impact of the COVID-19 pandemic on cancer care delivery included, but was not limited to, disruption of many routine health care services, and the rapid introduction of both social distancing and virtual care. Little was known about how COVID-19-related changes to cancer care delivery would impact patient satisfaction with care and care coordination. Objective: This report aims to present and interpret findings of an online survey of people with oncology-related conditions in Manitoba, Canada, during the COVID-19 pandemic, exploring patient satisfaction and care coordination. Methods: A link to an online survey was made available to patients receiving cancer treatment in Manitoba, Canada, between July 31, 2020, and February 28, 2022. The survey included validated patient-reported experience measures (PREMs) for patient satisfaction and care coordination. Analysis included the generation of descriptive statistics and logistic regression, including univariate and multivariate analysis using stepwise model building. The survey results were interpreted using fit theory as a theoretical lens. Results: A total of 203 responses were collected, of which 154 had completed responses for all PREM measures and were included in the analysis. Response rate is estimated at 3.3%-2.0%. The average age was 65 (SD 11.7) years. Most respondents were male (n=79, 52.7%). Most respondents were being treated with curative intent (n=81, 53.6%). The most common type of cancer was breast (n=41, 26.6%). Univariate analysis demonstrated that ages 60-69 years were associated with above average patient satisfaction (OR 2.205, 95% CI 1.045-4.624; P=.04). Age <60 years (OR 0.437, 95% CI 0.204-0.934; P=.03) and European Cooperative Group functional status (ECOG) ≥2 (OR 0.327, 95% CI 0.137-0.782; P=.01) were associated with below average patient satisfaction. Age <60 years, ECOG ≥2, and hematological cancer were selected through stepwise multivariate model building, resulting in an explanatory model (R2=0.129) of patient satisfaction. ECOG ≥2 was associated with below-average care coordination (OR 0.357, 95% CI 0.145-0.880; P=.03), and was the only identified predictor of care coordination, with no explanatory multivariate model generated. Fit theory supports that the level of patient satisfaction and care coordination in each population subset inversely reflects a relative level of unmet supportive care need. Conclusions: Survey respondents with poor functional status, those outside the 60-69 years age range, and those with nonhematological malignancies likely experience increased unmet supportive care needs compared with their counterparts. Further research is needed to determine whether these findings reflect transient phenomena related to the COVID-19 pandemic, selection biases associated with the survey method used, or underlying health care delivery inequities.
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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 | 0.000 | 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) | 0.000 | 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".