Evidence map›Paper›PMID 37191849›Full record

ArticleJournal of Korean medical science2023

A Novel Screening, Brief Intervention, and Referral to Treatment (SBIRT) Based Model for Mental Health in Occupational Health Implemented on Smartphone and Web-Based Platforms: Development Study With Results From an Epidemiologic Survey.

Jaehyun Kim, Chan-Woo Yeom, Hwang Kim, Dooyoung Jung, Hyun Jeong Kim, Hoon Jo, Sang Baek Koh, Bong-Jin Hahm

Open access · goldAbstract read
In one paragraph

Article in Journal of Korean medical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.2field-weighted citation impact, top 21% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 4 institutions in 1 country.

Jaehyun KimDepartment of Psychiatry, Korea Army Training Center District Hospital, Nonsan, Korea.ORCID https://orcid.org/0000-0002-6553-9563
Chan-Woo YeomDepartment of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Korea.ORCID https://orcid.org/0000-0003-2265-3291
Hwang KimDepartment of Design, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea.ORCID https://orcid.org/0000-0002-7814-2688
Dooyoung JungDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea.ORCID https://orcid.org/0000-0002-5381-4847
Hyun Jeong KimDepartment of Dental Anesthesiology, School of Dentistry, Seoul National University, Seoul, Korea.ORCID https://orcid.org/0000-0002-9265-7549
Hoon JoArtificial Intelligence Big Data Medical Center, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0003-3762-1540
Sang Baek KohDepartment of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0001-5609-6521
Bong-Jin HahmDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea.ORCID https://orcid.org/0000-0002-2366-3275
Seoul National University · KRYonsei University · KREulji University · KRSeoul National University Hospital · KR

Funding

Korea Health Industry Development Institute HS20C0110
6 · The paper itself

Abstract

backgroundWhile the importance of mental health is well-recognized in the field of occupational health, implementation of effective strategies in the workplace has been limited by gaps in infrastructure, program comprehensiveness, coverage, and adherence. The authors developed a Screening, Brief Intervention, and Referral to Treatment (SBIRT) model based occupational mental health intervention, and implemented in a web-based format with a smartphone application.

methodsThe SBIRT-based intervention was developed by a multidisciplinary team, including occupational health physicians, nurses, psychiatrists, and software developers. The following mental health areas were included, based on outcomes of an epidemiological survey conducted: insomnia, depression, anxiety, problematic alcohol use, and suicidal risk. The viability of the two-step evaluation process utilizing a combination of the brief version and the full-length version of the questionnaire was examined using responses from the survey. The intervention was adjusted according to the survey results and expert opinions.

resultsThe epidemiological survey included 346 employees who completed the long-form version of mental health scales. These data were the used to confirm the diagnostic value of using a combination of short-form and long-form version of the scales for screening in the SBIRT model. The model uses a smartphone application for screening, provision of psychoeducation, and for surveillance. The universal methods of the model ensure it can be implemented by all occupational managers, regardless of their specialization in mental health. In addition to the two-step screening procedure to identify employees at-risk for mental health problems, the model includes a stepped care approach, based on risk stratification, to promote mental health education, management, and follow-up for continuous care.

conclusionThe SBIRT model-based intervention provides an easy-to-implement approach for the management of mental health in the workplace. Further studies are required to examine the effectiveness and feasibility of the model.

Indexed as

Occupational HealthSubstance-Related DisordersCrisis InterventionHumansInternetMass ScreeningMental HealthReferral and ConsultationSmartphoneSurveys and QuestionnairesDigital TechnologyMental HealthOccupational HealthSBIRTScreeningSurveillance

Identifiers

PMID37191849
PMCPMC10186078
OpenAlexW4366504235

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.