Effects of the Canadian Suicide Prevention Service's Text Interventions on Texters' Emotions, Distress Relief, Perceived Abilities, and Practices Associated With Better Outcomes
Bibliographic record
Abstract
AIMS: To describe users of the Canada Suicide Prevention Service textline (now "988"), explore their perceived impact of the service and identify characteristics of interventions associated with a greater likelihood of positive effects of exchanges. METHODS: Data from 146 transcripts were analyzed using quantitative content analysis, and data were associated with counselor assessments and pre- and post-intervention questionnaire responses. Suicide risk was assessed using the Suicidal Ideation Attributes Scale (SIDAS). RESULTS: 78.8% of texters exhibited "severe" suicidal ideation on SIDAS, with 26.7% reporting specific plans for suicide. Complete risk assessments were often not conducted, but counselors extensively explored texters' resources and discussed potential solutions. Positive emotional changes were associated with counselors' thorough exploration of resources. Only one technique, "Reinforcing a strength or positive action of the texter," was significantly associated with positive outcomes. LIMITATIONS: Low response rates to post-intervention survey questions may affect the representativity of participants compared to all textline texters. CONCLUSION: A large proportion of texters reported they were less upset and were better able to cope with their problems after the text exchange. However, there is a need for more training and supervision to ensure that adequate suicide risk assessments are conducted, or the development of shorter assessment procedures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".