Use of New Technologies in the Prevention of Suicide in Europe: An Exploratory Study
Bibliographic record
Abstract
BACKGROUND: New technologies are an integral component of today's society and can complement existing suicide prevention programs. Here, we analyzed the use of new technologies in the prevention of suicide in 8 different European countries. OBJECTIVE: The aim of this paper was to assess the opinions of professionals in incorporating such resources into the design of a suicide prevention program for the region of Zamora in Spain. This investigation, encompassed within the European project entitled European Regions Enforcing Actions against Suicide (EUREGENAS), includes 11 regions from 8 different countries and attempts to advance the field of suicide prevention in Europe. METHODS: Using a specifically designed questionnaire, we assessed the opinions of 3 different groups of stakeholders regarding the use, frequency of use, facilitators, content, and format of new technologies for the prevention of suicide. The stakeholders were comprised of policy and public management professionals, professionals working in the area of mental health, and professionals related to the social area and non-governmental organizations (NGOs). A total of 416 participants were recruited in 11 regions from 8 different European countries. RESULTS: The utility of the new technologies was valued positively in all 8 countries, despite these resources being seldom used in those countries. In all the countries, the factors that contributed most to facilitating the use of new technologies were accessibility and free of charge. Regarding the format of new technologies, the most widely preferred formats for use as a tool for the prevention of suicide were websites and email. The availability of information about signs of alarm and risk factors was the most relevant content for the prevention of suicide through the use of new technologies. The presence of a reference mental health professional (MHP) was also considered to be a key aspect. The countries differed in the evaluations given to the different formats suggesting that the cultural characteristics of the country should be taken into account. CONCLUSIONS: New technologies are much appreciated resources; however they are not often underused in the field of suicide prevention. The results of this exploratory study show that new technologies are indeed useful resources and should be incorporated into suicide prevention programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".