Evidence map›Paper›PMID 40568211›Full record

ArticleFrontiers in medicine2025

A step toward the future? evaluating GenAI QPR simulation training for mental health gatekeepers.

Inbar Levkovich, Yuval Haber, Yossi Levi-Belz, Zohar Elyoseph

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Inbar LevkovichFaculty of Education, Tel Hai College, Upper Galilee, Israel.
Yuval HaberInterdisciplinary Studies Unit, Bar- Ilan University, Ramat Gan, Israel.
Yossi Levi-BelzRuppin Academic Center, Lior Tsarfati Mental Pain Center, Emek Hefer, Israel.
Zohar ElyosephFaculty of Eduction, University of Haifa, Mount Carmel, Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Suicide remains a leading cause of preventable death, placing a significant burden on healthcare systems worldwide. Effective suicide prevention relies not only on mental health professionals but also on well-trained gatekeepers, including primary care providers, emergency physicians, and community healthcare workers. Traditional training programs, such as Methods: A total of 89 adult participants from the community, all of whom were mental health professionals (including social workers, occupational therapists, speech therapists, and physicians), completed pre- and post-intervention assessments measuring self-efficacy and willingness to support individuals at risk of suicide. Participants engaged in real-time interactions with an AI-powered simulator that mimicked conversations with at-risk individuals, enabling dynamic practice of QPR (Question, Persuade, Refer) skills. Data were collected in June 2024. Quantitative data were analyzed using paired Results: Post-intervention self-efficacy scores showed a significant increase, with a large effect size (Cohen's D = 1.67). Willingness-to-support scores demonstrated a slight but non-significant improvement. Higher QPR self-efficacy correlated positively with increased willingness to support. Qualitative feedback indicated that participants found the simulator realistic and beneficial for skill acquisition, although some expressed concerns regarding the potential reduction of human interaction in mental health training. Conclusion: AI-driven simulators hold promise as scalable, accessible, and clinically relevant tools for suicide prevention training. Their integration into medical education and clinical settings could improve the preparedness of healthcare providers, primary care physicians, and frontline medical staff in identifying and managing suicide risk. These findings support the adoption of digital health innovations to enhance medical training and public health interventions.

Indexed as

artificial intelligencedigital healthemergency medicinegatekeeper trainingmedical simulationmental health professionalsprimary caresuicide prevention

Identifiers

PMID40568211
PMCPMC12187690

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.