Patient Experiences With Hospitalization Due to Diabetes in Alberta, Canada: A Cohort Study Using Survey and Administrative Data
Bibliographic record
Abstract
OBJECTIVES: Individuals living with diabetes are often hospitalized. Despite this, little is known about their experiences with hospital care. In this study, we examined the comprehensive experiences of patients hospitalized due to diabetes in Alberta, Canada, and compared them with those of patients hospitalized for other chronic conditions. METHODS: We conducted a retrospective cohort study that linked survey data with inpatient records. Survey data were collected using the Canadian Patient Experiences Survey-Inpatient Care (CPES-IC) instrument. Results from 37 questions were classified as percent in "top box," which reflects the most positive answer choice. We also examined the association between overall experience and demographic and clinical factors among those living with diabetes. RESULTS: Over a 7-year period, 12,593 surveys (2,288 with diabetes and 10,305 with other chronic conditions) were obtained. Patients hospitalized due to diabetes had lower "top-box" scores on 24 questions, higher scores on 3 questions, and the remaining 10 questions showed no difference between groups. Those hospitalized due to diabetes indicated potential areas for improvement. These included receiving information about their condition and about the admission process, the nighttime quietness of their hospital room, being informed about possible side effects of new medications, and pain control. Overall experience was also shown to vary according to demographic and clinical factors. CONCLUSIONS: We found that individuals hospitalized due to diabetes reported lower experience scores than those hospitalized due to other chronic conditions. Our findings may be used to develop strategies to improve the patient experience among this cohort.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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".