Reducing treatment delay for early intervention: evaluation of a community based crisis helpline
Bibliographic record
Abstract
BACKGROUND: A limited number of studies have assessed the pathways to care of patients experiencing psychosis for the first time. Helpline/clinic programs may offer patients who are still functional but have potential for crisis an alternative that is free from judgment. METHODS: In this study we report on patient calling a round-the-clock crisis helpline for suicide prevention supported by psychiatric facilities in Mumbai, India. Chi-square and test of mean differences were used to compare outcomes between first-episode patients and those with a previous history. RESULTS: Within five years, the helpline received 15,169 calls. Of those callers, 2341 (15.4%) experienced suicidal ideation. Two hundred and thirty four patients opting for counseling lasting 12 months agreed to a psychiatric assessment. Of those, 32 were fist time psychosis sufferers, whereas, 54 had previously been psychotic. Of all psychiatric assessments, the clinic received 94 patients with 'first-episode psychosis'. We found that the duration of illness was significantly shorter (17 vs. 28 months) and suicide attempts were fewer (16 vs. 21) in first-time psychosis sufferers compared to those with a treatment history. CONCLUSIONS: We conclude that some first-episode patients of schizophrenia and other disorders do access services by using helplines. We also argue that helplines may be somewhat immune to stigma, allowing patients a safe alternative when finding help.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".