Performance indicators in long term care:International studies on their nature and use
Bibliographic record
Abstract
The thesis focuses on the role of performance indicators in long-term care (LTC) systems, emphasizing their importance in monitoring and improving care quality. It highlights the challenges of using indicators due to variations across different countries and systems. The research includes empirical studies examining the nature and use of LTC performance indicators from an international perspective.<br/><b>Part I</b> explores performance indicators in multiple countries, showing how they are used to assess care quality. It reveals commonalities in monitored aspects, such as pressure ulcers and falls, but also differences in data collection and regulatory approaches. The analysis shows that many jurisdictions lack a comprehensive framework for LTC performance measurement.<br/><b>Part II</b> presents case studies on the impact of performance indicators. One study examines the effects of public reporting on care quality in Canada, suggesting that publishing indicators can drive improvements but is influenced by existing initiatives. Another study investigates how indicators could support crisis preparedness, analyzing COVID-19 outbreak management in Tuscany nursing homes. The findings suggest that not all indicators are equally relevant for crisis response.<br/>The thesis underscores the role of stakeholders as co-creators of performance intelligence. It calls for refining performance indicators, improving international comparability, and engaging stakeholders in designing effective monitoring systems. Policymakers are advised to integrate multiple improvement strategies rather than relying solely on performance measurement. Overall, the research provides insights to enhance the effectiveness, equity, and resilience of LTC systems worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".