Centredness in health care: A systematic overview of reviews
Bibliographic record
Abstract
INTRODUCTION: The introduction of effective, evidence-based approaches to centredness in health care is hindered by the fact that research results are not easily accessible. This is partly due to the large volume of publications available and because the field is closely linked to and in some ways encompasses adjoining fields of research, for example, shared decision making and narrative medicine. In an attempt to survey the field of centredness in health care, a systematic overview of reviews was conducted with the purpose of illuminating how centredness in health care is presented in current reviews. METHODS: Searches for relevant reviews were conducted in the databases PubMed, Scopus, Cinahl, PsychINFO, Web of Science and EMBASE using terms connected to centredness in health care. Filters specific to review studies of all types and for inclusion of only English language results as well as a time frame of January 2017-December 2018, were applied. RESULTS: The search strategy identified 3697 unique reviews, of which 31 were included in the study. The synthesis of the results from the 31 reviews identified three interrelated main themes: Attributes of centredness (what centredness is), Translation from theory into practice (how centredness is done) and Evaluation of effects (possible ways of measuring effects of centredness). Three main attributes of centeredness found were: being unique, being heard and shared responsibility. Aspects involved in translating theory into practice were sufficient prerequisites, strategies for action and tools used in safeguarding practice. Further, a variety and breadth of measures of effects were found in the included reviews. CONCLUSIONS: Our synthesis demonstrates that current synthesized research literature on centredness in health care is broad, as it focuses both on explorations of the conceptual basis and the practice, as well as measures of effects. This study provides an understanding of the commonalities identified in the reviews on centredness in healthcare overall, ranging from theory to practice and from practice to evaluation. PATIENT OR PUBLIC CONTRIBUTION: Patient representatives were involved during the initiation of the project and in decisions about its focus, although no patient or public representatives made direct contributions to the review process.
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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 | 0.037 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.034 | 0.035 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".