What’s in a name? Protocol for a bibliometric and content analysis of rehabilitation, reablement, reactivation, and restorative health care services
Bibliographic record
Abstract
Abstract Background: Various terms are used interchangeably to describe health care services that focus on supporting functional recovery after experiencing a health event. Previous literature has identified these terms as the 4R’s: rehabilitation, reablement, reactivation, and restorative health care services. However, there lacks a clear understanding and delineation between these concepts, making it difficult to measure the efficacy of each program type. This study protocol proposes a bibliometric and content analysis to map the current scientific literature within each 4R term. Methods: Using a predefined search strategy, we will identify and retrieve publications from databases Scopus and PubMed between the years 1924-2024 for each 4R concept. Two independent researchers will screen articles for eligibility. Bibliometric analyses will be conducted using RStudio software and Bibliometrix and Biblioshiny extensions. Bibliometric analyses will each include a performance analysis, citation analysis, co-citation analysis, bibliographic coupling, and co-word analysis to identify key research connections and emerging trends temporally and geographically. Bibliometric indicators of interest will include total publications, yearly output, author names, and countries, among others. In addition, we will also perform a qualitative content analysis to provide a more in-depth examination of the characteristics of each program type. Implications: Our line of inquiry intends to clarify the similarities and differences among the 4R terms to conceptualize each definition. Findings from this study have several implications for research, practice, and policy within the 4Rs, and can overall help to delineate these concepts and facilitate decision-making and resource allocation for these health care services. This study will reveal citation patterns, research connections, and foundation themes that can inform the suitability of practice transfer and resource allocation within and between rehabilitation fields. A methodological understanding of the 4R service types can inform decision-making on the patient, healthcare professional, and system level for each service.
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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 | 0.114 | 0.199 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.021 |
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".