A systematic compilation of rating scales developed, translated, and adapted in India
Bibliographic record
Abstract
Background: There is a lack of comprehensive data sources on various rating instruments that have been developed, translated, and adapted in Indian context. Aim: Accordinly, this review aimed to compile the available scales/questionnaires/instruments developed, adapted, and translated for use in India. Methods: For this, the search engines like PUBMED, Google Scholar, MedKnow, and Science Direct were searched for scales that have been developed, translated, and adapted in Indian context or an Indian language. Only articles reporting a scale/questionnaire development/interview schedules from India or in an Indian language were included. Results: Available data suggests that most of the instruments that have been translated in the Indian context have been done so in Hindi language. Very few instruments are available in other languages. The scales/instruments that are available in multiple languages include Mini International Neuropsychiatric Interview (MINI), General Health Questionnaire (GHQ), Patient Health Questionnaire (PHQ), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Edinburgh postnatal depression, Epworth sleepiness scale to evaluate daytime sleepiness, Columbia-Suicide Severity Rating Scale (C-SSRS), Recovery quality of life, World Health Organization Quality of Life-Bref version, Subjective happiness scale, Hospital Anxiety and Depression scale (HADS), Perceived Stress Scale (PSS), Multidimensional Scale of Perceived Social Support (MSPSS), Internalized Stigma of Mental Illness (ISMI), COVID-19 stigma scale, Dyadic adjustment scale, Broad Autism Phenotype Questionnaire (BAPQ), Strength and Difficulties Questionnaire (SDQ), and Rosenzweig picture frustration study (Children's form). Overall, very few instruments have been developed in India. Conclusion: To conclude, our review suggests that a limited number of scales have been developed in India and in terms of translation and adaptation, most of these have been done in Hindi only. Keeping these points in mind, there is a need to develop more psychometrically sound scales for research and routine clinical practice. Additionally, efforts must be made to translate and adapt scales available in different languages and subject the same to psychometric evaluations.
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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 | 0.033 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".