Situation Analysis of Cognitive Rehabilitation for Brain Injuries in Iran: A Mixed Method Study
Bibliographic record
Abstract
Background and purpose: Cognitive impairments significantly impact individuals' lives. Cognitive rehabilitation aims to enhance their physical, social, and occupational performance. This study evaluates the current state of cognitive rehabilitation in Iran and reports on related research and practices. Materials and methods: This mixed-method study utilized the Rehabilitation Service Assessment Tool (RSAT) tool, focusing on five main areas. Data collection included a comprehensive search of national resources (guidelines, regulations) and a scoping review of articles from PubMed and Scopus. Information on epidemiological data, intervention tools, and validated instruments was structured using the RSAT checklist. Results: Despite legal support for disabled individuals, cognitive impairments are not explicitly recognized, complicating planning and resource allocation. In higher education, notable efforts have been made for academic development. Of 1055 retrieved articles, 260 met the inclusion criteria, with over half focusing on epidemiological objectives. A total of 169 tools were identified, with the Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Stroop Test being the most used. A large number of studies were conducted in the past decade, accounting for 85.1% of the total, primarily by 62 national research institutions, with minimal private sector involvement. Conclusion: The aging population and high prevalence of brain injuries underline the urgent need for cognitive rehabilitation development in Iran. Significant gaps remain in policymaking, planning, resources, infrastructure, and evidence-based guidelines. Existing academic and private sector capacities, alongside recent research, provide a foundation for growth. Addressing these gaps requires collaborative efforts across legislative, educational, and research institutions.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".