A Review of Conceptual Models for Rehabilitation Research and Practice
Bibliographic record
Abstract
Background: Since rehabilitation practice focuses on working in multidisciplinary teams to optimize the physical, psychological, and social outcomes of the patient, conceptual models are extremely important in providing a theoretical basis for advancing scientific knowledge and improving professional practice. Aim: Although rehabilitation-related conceptual models have appeared in the literature more than fifty years ago or so, there has been no systematic efforts to review them. The purpose of this paper is to explore the existing rehabilitation models and to link these models to the ICF model of the World Health Organization. Methods: A structured literature search was performed in different databases including Medline and PubMed using terms such as: “rehabilitation” AND “Model” OR “Framework” OR “conceptualization”. 43 citations were identified and further evaluated by two judges according to pre-defined inclusion/exclusion criteria. Results: Six conceptual rehabilitation models were identified in the literature: the Biomedical Model, the Social Model, the Bio-Psycho-Social Model (BPS), the International Classification of Impairments, Disabilities, and Handicaps Model (ICIDH), the Community Based Rehabilitation Model (CBR), and the Health-Related Quality of Life Model (HRQoL). The concepts on which the models are built were linked to the International Classification of Functioning, Disability, and Health (ICF) domains. The strengths and limitations of each model are discussed. The majority of the concepts from the six models could be linked to the ICF model. Conclusion: By applying the conceptual models, an additional perspective can be added by rehabilitation therapists to multidisciplinary teams that use the ICF model. When relationships are highly complex, as they are in rehabilitation processes, it is challenging to develop models that are applied in different contexts. However, it is possible to establish relationships between different variables that are observable.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.031 | 0.037 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".