Discussion of Patient-Centered Care in Health Care Organizations
Bibliographic record
Abstract
The tradition of inherent knowledge and power of health care providers stands in stark contrast to the principles of self-determination and patient participation in patient-centered care. At the organizational level, patient-centered care is a merging of patient education, self-care, and evidence-based models of practice and consists of 4 broad domains of intervention including communication, partnerships, health promotion, and physical care. As a result of the unexamined discourse of knowledge and power in health care, the possibilities of patient-centered care have not been fully achieved. In this article, we used a critical social theory lens to examine the discursive influence of power upon the integration of patient-centered care into health care organizations. We begin with an overview of patient-centered care, followed by a discussion of the various ways that it has been introduced into health care organizations. We proceed by deconstructing the inherent power and knowledge of health care providers and shed light on how these long-standing traditions have impeded the integration of patient-centered care. We conclude with a discussion of viable solutions that can be used to implement patient-centered care into health care organizations. This article presents a perspective through which the integration of patient-centered care into health organizations can be examined.
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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 | 0.062 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.062 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".