Understanding and Measuring Patients' Assessment of the Quality of Nursing Care
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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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".