Consistency of priorities for quality improvement for nursing homes in Italy and Canada: A comparison of optimization models of resident satisfaction
Bibliographic record
Abstract
The paper seeks to identify aspects of care that may be easily modified to yield a desired level of improvement in residents' overall satisfaction with nursing homes, comparing data across Canada and Italy. Using a structured questionnaire, 681 and 1116 nursing home residents were surveyed in Ontario in 2009 and in Tuscany in 2012, respectively. Fourteen items were common to the surveys, including willingness to recommend (WTR), which was used as the dependent variable and measure of global satisfaction. The other analogous items were entered as covariates in ordinal logistic regression models predicting residents' WTR in each jurisdiction separately. Regression coefficients were then incorporated into a constrained nonlinear optimization problem selecting the most efficient combination of predictors necessary to increase WTR by as much as 15%. Staff-related aspects of care were selected first in the optimization models of each jurisdiction. In Ontario, to improve WTR the primary focus should be on staff relationships with residents, while in Tuscany it was the technical skill and knowledge of staff that was selected first by the optimization model. Different optimization solutions might mean that the strategies required to improve global satisfaction in one jurisdiction could be different than those for the other jurisdictions. The optimization model employed provides a novel solution for prioritizing areas of focus for quality improvement for nursing homes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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".