Person-centred rehabilitation: what exactly does it mean? Protocol for a scoping review with thematic analysis towards framing the concept and practice of person-centred rehabilitation
Bibliographic record
Abstract
INTRODUCTION: Person-centredness is a philosophy for organising and delivering healthcare based on patients' needs, preferences and experiences. Although widely endorsed, the concept suffers from a lack of detail and clarification, in turn accounting for ambiguous implementation and outcomes. While a conceptual framework based on a systematic review defines person/patient-centred care components (Scholl et al, 2014), it applies across healthcare contexts and may not be sensitive to the nuances of the rehabilitation of adults with physical impairments. Accordingly, this study aims to build a conceptual framework, based on existing literature, of what person-centredness means in the rehabilitation of adults with physical impairments in the clinical encounter and broader health service delivery. METHODS AND ANALYSIS: We will use a scoping review methodology. Searches on relevant databases will be conducted first, combining keywords for 'rehabilitation', 'person-centered' and associated terms (including patient preferences/experiences). Next, snowball searches (citation tracking, references lists) will be performed. Papers will be included if they fall within predefined selection categories (seen as most likely informative on elements pertaining to person-centred rehabilitation) and are written in English, regardless of design (conceptual, qualitative, quantitative). Two reviewers will independently screen titles and abstracts, followed by screening of the full text to determine inclusion. Experts will then be consulted to identify relevant missing papers. This can include elements other than the peer-reviewed literature (eg, book chapters, policy/legal papers). Finally, information that helps to build the concept and practice of person-centred rehabilitation will be abstracted independently by two reviewers and analysed by inductive thematic analysis to build the conceptual framework. DISSEMINATION: The resulting framework will aid clarification regarding person-centred rehabilitation, which in turn is expected to conceptually ground and inform its operationalisation (eg, measurement, implementation, improvement). Findings will be disseminated through local, national and international stakeholders, both at the clinical and service organisation levels.
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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 | 0.195 | 0.212 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.061 | 0.017 |
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".