Patient-centered care frameworks, models and approaches: An environmental scan
Bibliographic record
Abstract
Although the definition of patient-centered care (PCC) remains unclear, researchers and healthcare professionals describe the concept as treating the patient as a unique human being with consideration for their physical and psychosocial needs and emphasize the importance of shared-decision making between patients and healthcare professionals. However, discussion around the connection and overlap between PCC and patient and family engagement (PE) has been limited. Some authors describe PE as an operationalization of PCC, while others consider PE a type of PCC. An enhanced understanding of PCC might allow for improvements in implementing PE across healthcare systems. Insight into the operationalization of PCC at a practical level may be attained through exploring models and programs introduced by various governments. We conducted an environmental scan examining models, approaches, and programs of PCC implemented by the governments of nine developed countries at regional and national levels, aiming to understand better how PCC is operationalized. We found seven major themes indicative of critical features of PE within PCC models: 1) recruitment and representation; 2) training and staff engagement; 3) rapport and relationships; 4) tools and support; 5) compensation and reimbursement; 6) knowledge translation; and 7) evaluation. Finally, we comment on how well the included PCC models promote diversity and cultural competence while highlighting the importance of cultural sensitivity and discussing potential strategies to integrate PCC and PE into healthcare activities. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://theberylinstitute.org/experience-framework/). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.049 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".