Understanding and Measuring Patients' Assessment of the Quality of Nursing Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Traditionally, patients have been considered incapable of evaluating the quality of care they receive, leading to their minimal involvement. OBJECTIVE: To develop the Patient's Assessment of Quality Scale--Acute Care Version (PAQS-ACV) to provide a mechanism through which patients can evaluate meaningfully the nursing care they receive. METHODS: Developed from qualitative interviews with patients, the original 90-item PAQS-ACV was tested with 1,470 medical surgical patients in 43 units across seven hospitals. The typical patient was a married, 50-year-old, high school-educated patient hospitalized for the fourth time. Every 10th patient was asked to complete the PAQS-ACV 2 weeks later. RESULTS: After exploratory factor analysis, 45 items remained in five factors, accounting for 54% of the variance. Internal consistency estimates were above.83 for four of the five factors, with the fifth factor being.68. Test-retest reliability ranged from .58 to .71. Content validity was established and construct validity has been explored preliminarily by examining the relationship between the PAQS-ACV scores and patients' compliance. DISCUSSION: Although the PAQS-ACV is a relatively new measure of quality nursing care, it has met many criteria for an adequate measure of quality care. The instrument fills a void in the assessment of quality by including patients in the direct evaluation of the care received.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it