Berekenen van kwaliteitsindicatoren voor de thuiszorg: voorbeeld uit het ADHOC project, een vergelijking tussen thuiszorgorganisaties uit 11 Europese landen
Bibliographic record
Abstract
OBJECTIVES: To describe and calculate Home Care Quality Indicators from data of the European Aged in Home Care (ADHOC) project. With due regard for risk factors, home care agencies at country level have been compared with each other on quality of care. METHODS: The indicators of Home Care quality of care (HCQIs) are calculated based on methods that have been developed in the US and Canada. The values of these QIs are risk adjusted on the basis of odds ratios of covariates resulting from logistic regression analysis on the ADHOC sample. To enhance the comparison of QIs between countries we have used the method of percentile thresholds and QI aggregate sum measure related to those. RESULTS: Risk adjusted values of 22 Home Care Quality Indicators differed considerably between home care agencies in the eleven European countries that participated in ADHOC. The QI aggregate showed which countries probably had the best home care and which had the worst. CONCLUSIONS: There are quality indicators available, derived from data of the Resident Assessment Instrument for Home Care, with which quality of care between home care agencies in and across nations can be adequately compared. Examples of this type of indicator are: social isolation, inadequate pain control, failure to improve in impaired locomotion in the home.
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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 | 0.060 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".