Public reporting of performance measures in long-term care in Canada: does it make a difference?
Bibliographic record
Abstract
Abstract Background Evidence of the impact of public reporting of healthcare performance on quality improvement is not yet sufficient to draw conclusions with certainty, despite the important policy implications. This study explored the impact of implementing public reporting of performance indicators of long-term care facilities in Canada. The objective was to analyse whether improvements can be observed in performance measures after publication. Methods We considered 16 performance indicators in long-term care in Canada, 8 of which are publicly reported at a facility level, while the other 8 are privately reported. We analysed data from the Continuing Care Reporting System managed by the Canadian Institute for Health Information and based on information collection with RAI-MDS 2.0 © between the fiscal years 2011 and 2018. A multilevel model was developed to analyse time trends, before and after publication, which started in 2015. The analysis was also stratified by key sample characteristics, such as the facilities' jurisdiction, size, urban or rural location and performance prior to publication. Results Data from 1087 long-term care facilities were included. Among the 8 publicly reported indicators, the trend in the period after publication did not change significantly in 5 cases, improved in 2 cases and worsened in 1 case. Among the 8 privately reported indicators, no change was observed in 7, and worsening in 1 indicator. The stratification of the data suggests that for those indicators that were already improving prior to public reporting, there was either no change in trend or there was a decrease in the rate of improvement after publication. For those indicators that showed a worsening trend prior to public reporting, the contrary was observed. Conclusions Our findings suggest public reporting of performance data can support change. The trends of performance indicators prior to publication appear to have an impact on whether further change will occur after publication. Key messages Public reporting is likely one of the factors affecting change in performance in long-term care facilities. Public reporting of performance measures in long-term care facilities may support improvements in particular in cases where improvement was not observed before publication.
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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 | 0.076 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".