A Systematic Review of Surveys for Measuring Patient-centered Care in the Hospital Setting
Bibliographic record
Abstract
BACKGROUND: Patient-centered care (PCC) is a core component of quality care and its measurement is fundamental for research and improvement efforts. However, an inventory of surveys for measuring PCC in hospitals, a core care setting, is not available. OBJECTIVE: To identify surveys for assessing PCC in hospitals, assess PCC dimensions that they capture, report their psychometric properties, and evaluate applicability to individual and/or dyadic (eg, mother-infant pairs in pregnancy) patients. RESEARCH DESIGN: We conducted a systematic review of articles published before January 2019 available on PubMed, Web of Science, and EBSCO Host and references of extracted papers to identify surveys used to measure "patient-centered care" or "family-centered care." Surveys used in hospitals and capturing at least 3 dimensions of PCC, as articulated by the Picker Institute, were included and reviewed in full. Surveys' descriptions, subscales, PCC dimensions, psychometric properties, and applicability to individual and dyadic patients were assessed. RESULTS: Thirteen of 614 articles met inclusion criteria. Nine surveys were identified, which were designed to obtain assessments from patients/families (n=5), hospital staff (n=2), and both patients/families and hospital staff (n=2). No survey captured all 8 Picker dimensions of PCC [median=6 (range, 5-7)]. Psychometric properties were reported infrequently. All surveys applied to individual patients, none to dyadic patients. CONCLUSIONS: Multiple surveys for measuring PCC in hospitals are available. Opportunities exist to improve survey comprehensiveness regarding dimensions of PCC, reporting of psychometric properties, and development of measures to capture PCC for dyadic patients.
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 | 0.041 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".