Defining and implementing patient-centered care: An umbrella review
Bibliographic record
Abstract
OBJECTIVES: Patient- or person-centered care (PCC) integrates people's preferences, values, and beliefs into health decision-making. Gaps exist for defining and implementing PCC; therefore, we aimed to identify core elements of PCC and synthesize implementation facilitators and barriers. METHODS: We conducted an overview of systematic reviews (umbrella review) and included peer-reviewed literature for adults in community/primary care settings. Two reviewers independently screened at Level 1 and 2, extracted data and appraised the quality of reviews. Three reviewers conducted a thematic analysis, and we present a narrative synthesis of findings. RESULTS: There were 2371 citations screened, and 10 systematic reviews included. We identified 10 PCC definitions with common elements, such as patient empowerment, patient individuality, and a biopsychosocial approach. Implementation factors focused on communication, training healthcare providers, and organizational structure. CONCLUSIONS: We provide a synthesis of key PCC elements to include in a future definition, and an overview of elements to consider for implementing PCC into practice. We extend existing literature by identifying clinician empowerment and culture change at the systems-level as two future areas to prioritize to enable routine integration of PCC into practice. PRACTICE IMPLICATIONS: Findings may be useful for researchers and or health providers delivering and evaluating PCC.
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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 | 0.070 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".