Factors that influence cancer patients’ overall perceptions of the quality of care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: This study outlines predictors of cancer patients' overall perceptions of the quality of care. DESIGN AND SETTING: Our sample included 2790 patients who received cancer care services during 2004 in 15 comprehensive cancer programmes across Ontario, Canada. Patients were classified into three groups: those receiving both chemotherapy and radiotherapy (n = 752), those receiving only chemotherapy (n = 1044), and those receiving only radiotherapy (n = 994). An ordinal logistic regression model for each patient group was performed to determine which variables most affected the probabilities of the patients' overall evaluations of the quality of care. Potential control variables were patients' age, sex, type of cancer, self-assessed health, and who completed the survey. RESULTS: Among seven common predictors of the overall quality perception across the three models, four should be of particular interest because patients perceived them as relatively problematic aspects of care. These are 'was informed about follow-up care after completing treatment', 'knew next step in care', 'knew who to go to with questions', and 'providers were aware of test results'. These predictors explained between 25 and 34% of the variance (depending on the model) of the overall perception of quality. The explanatory power of these predictors did not change across sex and age group. 'Self-assessed health' was the only control variable that remained in all three models. CONCLUSIONS: From a practical perspective, improvement efforts are best focused on factors that are strong predictors as well as on those for which there is a low score. Thus, on the basis of this study, practitioners' improvement efforts might be constructively focused on the four predictors mentioned above.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it