Exploring the Perceptions of Indian Mental Health Professionals Regarding Areca (Betel) Nut Products: A Cross-sectional Study
Bibliographic record
Abstract
Background: Areca nut (AN) and AN products (ANPs) are commonly used as psychoactive substances with marked dependence potential. Scant information exists on the Indian mental health professionals' (MHPs) knowledge of AN-ANPs, attitude toward AN-ANP use/users, and behavior regarding their clients' AN-ANP use. To address this gap, a survey was undertaken to assess MHPs' knowledge, attitudes, and behavioral responses toward AN-ANP use and addiction. Methods: We developed a pretested, customized questionnaire and conducted a cross-sectional online survey among a random sample of MHPs. Results: The 209 respondents included 91 psychiatrists, 105 clinical psychologists, and 13 other MHPs from diverse settings. Among them, 46.89% believed that AN-ANP use does not fit the definition of abuse/addiction as per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition or International Classification of Diseases 10th Revision (ICD-10)/ICD-11. Among the psychiatrists, clinical psychologists, and other MHPs, 60.4%, 48.6%, and 61.5% were unaware of any AN-ANP cessation protocols. The addictive potential of AN-ANP with tobacco was rated as severe by 68.1% of psychiatrists and 51.4% of clinical psychologists; 46.2% of other MHPs rated it as moderate. The addictive potential of AN-ANP without tobacco was rated as moderate by 50.5% of clinical psychologists and mild by 46.2% of psychiatrists. Of the sample, 67.46% discussed the harmful effects of AN-ANPs with clients, while 74.6% said a few or none of their clients sought help for AN-ANP cessation. Conclusion: Major lacunae were detected in the understanding of Indian MHPs about the addictive potential of AN-ANPs, management aspects, etc. An urgent need has been revealed for sensitization programs on AN-ANPs and the development of evidence-based cessation protocols.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".