Healing Mind: Mental health, Indigenous healers and Traditional Medicine
Bibliographic record
Abstract
Mental illnesses and other psychosocial disabilities are main source of burden for the family. About 13.7 per cent of general population of India has issues of mental illness or disorders and 10.6 per cent of these people need immediate intervention for the treatment followed by 10 per cent of the population has common mental disorders with 1.9 per cent people suffers with severe mental disorders. The mental morbidity is very high in urban areas. There is a higher prevalence of neurotic or stress-related disorders, schizophrenia and mood disorders. This situation may be due to fast-life styles, experiencing stress, complexities of living, a breakdown of support systems and challenges of economic instability. There are few population-level insights into the use of traditional healers and other forms of alternative care for the treatment of common mental disorders in our country as well as in South Asian and sub-Saharan Africa countries. Paper examines the extent to which alternative practitioners are consulted, and predictors of traditional healer visits for various neuropsychiatric diseases. Further it illuminates studies and national survey conducted on mental health on tribal communities and the role of traditional healers in the country. Paper raised the issue of mental illnesses as prevalent in the country and questions that do indigenous healing matters in healing the mind for better mental health in our country in first part of the paper besides analyzing recent survey, NMHS-2014. Then illuminates various mind healing ways and put it in question whether we can protect these healers and their medicines in existing IPR in part-II followed by traditional knowledge, medicines, National policy and cases of Indian patents and Intellectual Property Rights (IPR) in the last part where we conclude countries like Canada, South Africa or New Zeeland have supported for integration of traditional healers with main stream health care system. There are fears also generated for example in Canada that increasing acceptance of traditional healing may result into sea change. The lessons of these countries may be useful for our planning for integration of healers and their medicine in our country for effective resources utilization of the traditional healers and their medicines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".