A qualitative study on measuring patient‐centered care: Perspectives from clinician‐scientists and quality improvement experts
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND AIMS: Patient-centered care (PCC) benefits patients, health-care providers, and health-care systems by providing delivery of care that addresses patient values and needs while improving provider experiences, and by decreasing health-care expenditure. To improve PCC, health-care systems need to measure it. Recently, we developed a PCC framework that is evidence based and patient informed. The purpose of this study was to gather the perspective of clinician-scientists and quality improvement experts regarding the PCC domains included in the framework. Their perspectives were used to refine these domains, which ultimately will inform the development of PCC quality indicators. METHODS: Participants were recruited via expert and snowball sampling. Semi-structured interviews were conducted with clinician-scientists and quality improvement experts from Canada, the United States, and the United Kingdom from October 2017 to January 2018. With the use of an interview guide developed using the PCC framework, interviews were audio recorded and transcribed for a thematic analysis using NVivo qualitative data analysis software. Inductive thematic analysis was used to identify themes and subthemes. RESULTS: Sixteen semi-structured interviews were conducted, which included four clinician-scientists and 12 quality improvement experts. Twelve of the participants were from Canada, three from the United Kingdom, and one from the United States. From the thematic analysis, three major themes were identified: (a) measurability of PCC, (b) practical considerations for implementing measurement, and (c) policy and practice implications. Participants discussed barriers and recommendations to improve and increase the clarity of the PCC domains in health system reporting, resulting in several future directions to refine and target specific PCC domains. CONCLUSION: Clinician-scientists and quality improvement experts provided key recommendations for the measurement of PCC. The perspectives of key stakeholders in PCC measurement will inform strategies for the implementation and uptake of patient-centered quality indicators in health-care systems. The views of these key experts can lay the foundation for the development of standardized measures of PCC, to ensure monitoring and improvement of PCC.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| 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