Evidence map›Paper›PMID 41648786›Full record

ArticleFrontiers in psychiatry2026

Suicide prevention in Japanese cancer care hospitals: a topic modeling analysis of manuals and workflows.

Fumiko Kumada, Ken Kurisu, Masako Okamura, Tatsuo Akechi, Yumi Matsumura, Yosuke Uchitomi, Maiko Fujimori

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

7 authors.

Fumiko KumadaDivision of Survivorship Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan.
Ken KurisuDivision of Survivorship Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan.
Masako OkamuraDivision of Survivorship Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan.
Tatsuo AkechiDepartment of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Yumi MatsumuraDepartment of Patient Safety, Kyoto University Hospital, Kyoto, Japan.
Yosuke UchitomiDepartment of Cancer Survivorship and Digital Medicine, The Jikei University School of Medicine, Tokyo, Japan.
Maiko FujimoriDivision of Survivorship Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: People with cancer in Japan have an elevated risk of suicide, yet little information is available regarding in-hospital suicide prevention manuals and workflows. This study investigated the strategies and frameworks described in manuals and workflows developed by Designated Cancer Care Hospitals. Methods: Using snowball sampling, suicide prevention manuals and workflows for patients with cancer were collected from 14 Designated Cancer Care Hospitals. The textual data were quantitatively analyzed using topic modeling, specifically latent Dirichlet allocation, a text-mining technique. Topics were extracted and labeled through discussions with suicide prevention experts. Based on these topics, we proposed a set of recommended components for suicide prevention manuals and workflows for people with cancer. Results: In total, 329 paragraphs from the manuals and workflows of 13 hospitals were analyzed, yielding 20 topics. Among these, six topics were commonly addressed across hospitals, covering prevention (e.g., "Types of Suicide Hotspots and Examples of Countermeasures"), intervention (e.g., "Procedures and Collaboration System for Managing Patients with Suicidal Ideation"), and postvention (e.g., "On-Site Response Procedures Upon Discovering a Suicide"). In addition, six topics were described in greater length by certain hospitals, reflecting their unique institutional characteristics (e.g., "Overview of Suicide Prevention Measures"). Conclusions: Commonly shared topics should be prioritized in suicide prevention manuals and workflows for patients with cancer. Conversely, hospital-specific topics may serve as useful references tailored to the distinct characteristics of each hospital. Factors such as hospital size, geographic location, and clinical practice should be considered when determining the content of these manuals and workflows.

Indexed as

cancerhealth services administrationhospitalsneoplasmssuicide preventiontopic modeling

Identifiers

PMID41648786
PMCPMC12869307

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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 Socratic graph.