ViolHelp: results of a pilot study to identify potential warning signs and risk factors for self- and hetero-directed violence in the calls received by the Helplines of the Italian National Institute of Health.
Bibliographic record
Abstract
BACKGROUND: Self- and hetero-directed violence (SHDV) is a serious public health problem and a complex phenomenon, influenced by individual and environmental factors. SHDV may occur particularly in moments of personal, economic and/or social crisis. During the COVID-19 pandemic, the ISS-Helplines operators have perceived an increase in psychological distress and self-isolation among callers. The ViolHelp project aimed at identifying potential warning signs and risk factors of SHDV emerging in the activity of the ISS-Helplines (Istituto Superiore di Sanità, ISS, Italian National Institute of Health). MATERIALS AND METHODS: A dashboard collecting warning signs and risk factors of SHDV was developed to be used during the ISS-Helplines activity. RESULTS: In one year of data collection, 135 calls were compiled. In 106 calls, callers referred experienced violence: 72 self-directed violence (SDV), 20 hetero-directed violence (HDV), 14 both. The most frequent warning signs and risk factors for SDV were desire to die (68.6%), previous suicide attempts (31.4%) and threat of self-harm (25.6%); for HDV were depressed mood (32.4%), diagnosis of pathology and/or psychiatric disorders, desire to die, use of psychotropic drugs, and alcohol abuse (29.4%). CONCLUSIONS: The results of this pilot project show the importance of being able to read the warning signs and to create a network that can improve information, prevention and support activities for people at risk of violence and their families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".