Patient-Nurse Interrater Reliability and Agreement of the Richards-Campbell Sleep Questionnaire
Bibliographic record
Abstract
BACKGROUND: The Richards-Campbell Sleep Questionnaire (RCSQ) is a simple, validated survey instrument for measuring sleep quality in intensive care patients. Although both patients and nurses can complete the RCSQ, interrater reliability and agreement have not been fully evaluated. OBJECTIVES: To evaluate patient-nurse interrater reliability and agreement of the RCSQ in a medical intensive care unit. METHODS: The instrument included 5 RCSQ items plus a rating of nighttime noise, each scored by using a 100-mm visual analogue scale. The mean of the 5 RCSQ items comprised a total score. For 24 days, the night-shift nurses in the medical intensive care unit completed the RCSQ regarding their patients' overnight sleep quality. Upon awakening, all conscious, nondelirious patients completed the RCSQ. Neither nurses nor patients knew the others' ratings. Patient-nurse agreement was evaluated by using mean differences and Bland-Altman plots. Reliability was evaluated by using intraclass correlation coefficients. RESULTS: Thirty-three patients had a total of 92 paired patient-nurse assessments. For all RCSQ items, nurses' scores were higher (indicating "better" sleep) than patients' scores, with significantly higher ratings for sleep depth (mean [SD], 67 [21] vs 48 [35], P = .001), awakenings (68 [21] vs 60 [33], P = .03), and total score (68 [19] vs 57 [28], P = .01). The Bland-Altman plots also showed that nurses' ratings were generally higher than patients' ratings. Intraclass correlation coefficients of patient-nurse pairs ranged from 0.13 to 0.49 across the survey questions. CONCLUSIONS: Patient-nurse interrater reliability on the RCSQ was "slight" to "moderate," with nurses tending to overestimate patients' perceived sleep quality.
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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 | 0.045 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".