International field test results of the Observable Indicators of Nursing Home Care Quality instrument
Bibliographic record
Abstract
Researchers at the University of Missouri-Columbia developed the Observable Indicators of Nursing Home Care Quality instrument to measure the dimensions of nursing home care quality during a brief on-site visit to a nursing home. The instrument has been translated for use in Iceland and used in Canada. Results of the validity and reliability studies using the instrument in 12 nursing homes in Reykjavik, in a large Veterans Home in Ontario with 14 units tested separately, and in 20 nursing homes in Missouri, are promising. High-content validity was observed in all countries, together with excellent inter-rater reliability and coefficient alpha. Test-retest reliabilities in Iceland and Missouri were good. Results of the international field test of the Observable Indicators of Nursing Home Care Quality instrument points to the usefulness of such an instrument in measuring nursing home care quality following a quick on-site observation in a nursing facility. The instrument should be used as a facility-wide assessment of quality, rather than for individual units within a facility. We strongly recommend its use by practising nurses in nursing homes to assess quality of care and guide efforts to improve care. We recommend its use by researchers and consumers and further testing of the use of the instrument with regulators.
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 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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".