Suicide prevention competencies among urban Indian physicians: A needs assessment
Bibliographic record
Abstract
INTRODUCTION: India accounts for the highest estimated number of suicides in the World. In 2012, more than 258,000 of the 804,000 suicide deaths worldwide occurred in India. Early identification and effective management of suicidal ideation and behavior are paramount to saving lives. However, mental health resources are often scarce and limited. Throughout India, there is a severe shortage in mental health professions trained, which results in a treatment gap of about 90%. A comprehensive needs assessment was undertaken to identify the nature of the deficits in suicide prevention training for physicians in three Indian cities: Mumbai, Ahmedabad, and Mysore. MATERIALS AND METHODS: The study was carried out in several concurrent phases and used a mixed-method approach of converging quantitative and qualitative methodologies. Data were collected using survey questionnaires, focus groups, consultations, and environmental scans. A total of 46 physicians completed the questionnaire. Focus groups were conducted in Mumbai and Ahmedabad with 40 physicians. Consultations were carried out with psychiatrists and psychiatric residents from hospitals and clinics in Mumbai, Ahmedabad, and Mysore. RESULTS: Training gaps in suicide prevention exist across the health care professions. Existing training lacks in both quality and quantity and result in critical deficits in core competencies needed to detect and treat patients presenting with suicidal ideation and behavior. Only 43% of the surveyed physicians felt they were competent to treat suicidal patients. The majority of surveyed physicians believed they would greatly benefit from additional training to enhance their suicide risk assessment and intervention skills. CONCLUSIONS: There is a dire need for medical schools to incorporate suicide prevention training as a core component in their medical curricula and for continuing medical education training programs for physicians to enhance competencies in early detection and management of suicidal behavior.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".