Drivers of paediatric inpatient experience: retrospective analysis of casemix factors for the Alberta Paediatric Inpatient Experience Survey in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: In Alberta, the Alberta Paediatric Inpatient Experience Survey (APIES) is used as a proxy-reported measure of paediatric experience. To our knowledge, the influence of casemix factors on patient experience as measured by paediatric patient experience surveys have not been reported within Canadian paediatric samples. In this paper, we sought to determine the patient and respondent factors associated with paediatric inpatient experiences in Alberta, Canada. DESIGN: Retrospective analysis of patient experience survey data. SETTING: Inpatiet acute care hospitals in Alberta, Canada. INTERVENTION AND MAIN OUTCOME MEASURES: tests were performed to assess distribution of casemix between general and paediatric hospitals. Logistic regression was performed with overall hospital experience as the dependent variable with casemix and hospital variables as independent variables. RESULTS: Casemix characteristics were unevenly distributed between general and paediatric hospitals. Compared with reference categories, older respondents, healthier patients and treatment at paediatric facilities had increased odds of providing most-positive ratings. Increased respondent education was associated with decreased odds of providing most-positive ratings. Likelihood-ratio tests showed that most casemix variables improved model fit, except for respondent relationship to the patient. CONCLUSIONS: To improve reports of paediatric inpatient experience, administrators and providers require reliable and comparable measurement. Both the Child Hospital Consumer Assessment of Healthcare Providers and Systems and other measures of patient and family experience need to consider patient and respondent characteristics when interpreting results. Considered with other research from patient experience in Alberta, we discuss future directions and quality improvement implications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.001 | 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".