Evaluation of the Effectiveness of Suicide.ca, Quebec’s Digital Suicide Prevention Strategy Platform: Cross-Sectional Descriptive Study
Bibliographic record
Abstract
BACKGROUND: In 2017, the Quebec government assigned the Association québécoise de prévention du suicide (AQPS) to develop a digital suicide prevention strategy (DSPS). The AQPS responded by creating a centralized website that provides information on suicide and mental health, identifies at-risk individuals on the internet, and offers direct crisis intervention support via chat and text. OBJECTIVE: This study aims to evaluate the effectiveness of suicide.ca, Quebec's DSPS platform. METHODS: This study used a cross-sectional descriptive design. The study population comprised internet users from Quebec, Canada, who visited the suicide.ca platform between October 2020 and October 2021. Various data sources, such as Google Analytics, Firebase Console, and Customer Relation Management data, were analyzed to document the use of the platform. To understand the profile of suicide.ca users, frequency analyses were conducted using data from the self-assessment module questionnaires, the intervention service's triage questionnaire, and the counselors' intervention reports. The effectiveness of the platform's promotional activities on social media was assessed by examining traffic peaks. Google Analytics was used to evaluate the effectiveness of AQPS' strategy for identifying at-risk internet users. The impact of the intervention service was evaluated through an analysis of counselors' intervention reports and postintervention survey results. RESULTS: The platform received traffic from a diverse range of sources, with promotional efforts on social media directly contributing to the increased traffic. The requirement of a user account posed a barrier to the use of the mobile app, and a triage question that involved personal information led to a substantial number of dropouts during the intervention service triage. AdWords campaigns and fact sheets addressing suicide risk factors played a crucial role in driving traffic to the platform. With regard to the profile of suicide.ca users, the findings revealed that the platform engaged individuals with diverse levels of suicidal risk. Notably, users of the chat service displayed a higher suicide risk than those who used the self-assessment module. Crisis chat counselors reported a positive impact on approximately half of the contacts, and overall, intervention service users expressed satisfaction with the support they received. CONCLUSIONS: A centralized digital platform can be used to implement a DSPS, effectively reaching the general population, individuals with risk factors for suicide, and those facing suicidal issues.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".