Bibliographic record
Abstract
Background: Importance of community rehabilitation in India has been emphasized in previous research. There is ample research that has been published for different communities in the country. However, the precise role of physiotherapy in community rehabilitation is unclear.The objective of the current brief report is to look into the role of physiotherapy in community rehabilitation. Methods: Relevant literature search was done using databases namely Medline, Scopus, PubMed, PEDro and CINAHL using search terms- India, community rehabilitation, home rehabilitation,home exercises and physiotherapy. Studies that followed the PICO format, published in English,after 2005 and that had specifically mentioned the role of physiotherapy in community projects were included. Results: While there are handful of studies that have mentioned the contribution of physiotherapy in the community, most of the interventions are targeted toward management of chronic health conditions. More work needs to be done to outline the importance and precise role of physiotherapy in the rehabilitation of communities in India, especially in preventive care.A model has been created to emphasize the holistic approach of physiotherapy in the Indian setting. Conclusion: Physiotherapy has a pivotal position in community rehabilitation in India.However, published research for the same is lacking. While physiotherapy interventions have been designed to target chronic health conditions in the community, emphasis on preventive care is lacking.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".