Effect of helping suicidal people using text messaging: An evaluation of effects and best practices of the Canadian suicide prevention Service's text helpline
Bibliographic record
Abstract
BACKGROUND: Empirical research on best practices in suicide prevention text intervention is scarce. We present analyses of exchanges concerning suicide on the Canadian Suicide Prevention Service (CSPS) text helpline. OBJECTIVE: To describe the users of the CSPS text service, explore the perceived impact of the service, and identify intervention characteristics associated with a greater likelihood of positive or negative effects of the exchanges. METHODS: We analyzed data from 112 transcripts using quantitative content analysis, counselor assessments of the calls, and responses by callers to pre-call questionnaires. RESULTS: Counselors infrequently conducted a complete suicide risk assessment, but almost always thoroughly explored resources and discussed possible solutions to callers' problems. An operational action plan was rarely developed. Only one technique, reinforcing a strength or a positive action of the caller, was a significant predictor of positive effects of the call. The number of words exchanged during the intervention was positively correlated with the completeness of explorations of resources and solutions and the development of an action plan. CONCLUSIONS: High-quality effective interventions can be delivered via text messages. Using reinforcement of strengths and encouraging longer calls is recommended. Intervention effects were comparable to those reported in studies of telephone and chat services.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".