Addressing Changing Healthcare Needs: A Realist‐Inspired Review of Innovative Rehabilitation Care Models
Bibliographic record
Abstract
RATIONALE/OBJECTIVES: Canadian healthcare is facing an aging population, an increasing prevalence of chronic disease and related disability, and rising healthcare costs. Integrating innovative rehabilitation models of care may help bolster health systems by shifting to a longer-term approach to addressing health and wellbeing. However, little is known about how these care models may look and what is needed to ensure their effective operationalization in practice. METHODS: This realist-inspired narrative review explored how, when, and in what circumstances innovative models of care have been successfully implemented and sustained in rehabilitation. The peer-reviewed and grey literature was searched and subsequently screened by title, abstract, and full text. Data extracted from included articles focused on identifying contexts, mechanisms, and outcomes. A numerical analysis of quantitative data and a conventional content analysis of qualitative abstractions was conducted. RESULTS: Twenty-six documents published between 2014 and 2021 were uncovered predominantly from Australia and Canada. Overall, for new care models to be successfully implemented and sustained, they need to: (1) have clearly articulated goals, (2) have access to short- and long-term funding, (3) align with key legislative changes to optimise buy-in, (4) take a multidisciplinary approach that is supported by management, and (5) include educational and outreach strategies that can be implemented amongst all interested parties. CONCLUSIONS: The heterogeneity of studies and limitations in their reporting precluded the identification of context-mechanism-outcome configurations typically found in realist reviews. Future implementation research should draw on relevant reporting guidelines to report their findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".