Person‐centred health care: a critical assessment of current and emerging research approaches
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Person-centred health care is prominent in international health care reforms. A shift to understanding and improving personal care at the point of delivery has generated debates about the nature of the person-centred research agenda. This paper purviews research paradigms that influence current person-centred research approaches and traditions that influence knowledge foundations in the field. It presents a synthesis of the emergent approaches and methodologies and highlights gaps between static academic research and the increasing accessibility of evaluation, informatics and big data from health information systems. FINDINGS: Paradigms in health services research range from theoretical to atheoretical, including positivist, interpretive, postmodern and pragmatic. Interpretivist (subjective) and positivist (objectivist) paradigms have been historically polarized. Yet, integrative and pragmatic approaches have emerged. Nevertheless, there is a tendency to reductionism, and to reduce personal experiences to metrics in the positivist paradigm. Integrating personalized information into clinical systems is increasingly driven by the pervasive health information technology, which raises many issues about the asymmetry and uncertainty in the flow of information to support personal health journeys. The flux and uncertainty of knowledge between and within paradigmatic or pragmatic approaches highlights the uncertainty and the 'unorder and disorder' in what is known and what it means. Transdisciplinary, complex adaptive systems theory with multi-ontology sense making provides an overarching framework for making sense of the complex dynamics in research progress. CONCLUSION: A major challenge to current research paradigms is focus on the individualizing of care and enhancing experiences of persons in health settings. There is an urgent need for person-centred research to address this complex process. A transdisciplinary and complex systems approach provides a sense-making framework.
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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 | 0.367 | 0.299 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.034 | 0.019 |
| Science and technology studies | 0.016 | 0.102 |
| Scholarly communication | 0.046 | 0.062 |
| Open science | 0.013 | 0.025 |
| Research integrity | 0.018 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".