Impact of patient characteristics on the Canadian Patient Experiences Survey–Inpatient Care: survey analysis from an academic tertiary care centre
Bibliographic record
Abstract
OBJECTIVE: To determine the role of patient demographics, care domains and self-perceived health status in the analysis and interpretation of results from the Canadian Patient Experience Survey-Inpatient Care. DESIGN: Cross-sectional survey. SETTING: Single large Canadian two campus tertiary care academic centre. PARTICIPANTS: Random sampling of hospital patients postdischarge. INTERVENTION AND MAIN OUTCOME MEASURES: Logistic regression models were developed to analyse topbox scoring on four questions of global care (rate experience, recommend hospital, rate hospital, overall helped). Means of each composite domain were correlated to the four overall scores at the patient level to determine Spearman's rank correlation coefficients which were plotted against the overall (hospital) domain score for the key driver analysis. RESULTS: Topbox scoring was decreased with worse degrees of perceived physical and mental health in all four global questions (p<0.05). Female gender and higher levels of education were associated with worse scoring on rate experience, recommend hospital and rate hospital (p<0.001). Whereas there was a significant difference between hospital departments in unadjusted measures, these differences were no longer evident after adjustment with patient covariates. Key driver analysis identified person-centred care, care transition and the domain related to emergency admission as areas of highest potential for improvement. CONCLUSIONS: Global measures of overall care are influenced by patient-perceived physical and mental health. Caution should be exercised in using patient-satisfaction surveys to compare performance between different healthcare provision entities, as apparent differences could be explained by variation in patient mix rather than variation in performance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".