Evidence map›Paper›PMID 38466615›Full record

ArticleScandinavian journal of work, environment & health2024

Change in employment status and its causal effect on suicidal ideation and depressive symptoms: A marginal structural model with machine learning algorithms.

Jaehong Yoon, Ji-Hwan Kim, Yeonseung Chung, Jinsu Park, Ja-Ho Leigh, Seung-Sup Kim

Abstract read
In one paragraph

Article in Scandinavian journal of work, environment & health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Jaehong Yoon
Ji-Hwan Kim
Yeonseung Chung
Jinsu Park
Ja-Ho Leigh
Seung-Sup KimDepartment of Environmental Health Sciences, Seoul National University, Room 718, Bldg 220, Gwanak-ro 1, Seoul 08826, Republic of Korea. kim.seungsup@snu.ac.kr.

Funding

Ministry of Education of the Republic of Korea and the National Research Foundation of Korea NRF-2023S1A5A2A03084402
6 · The paper itself

Abstract

objectiveThis study aimed to assess the causal effect of a change in employment status on suicidal ideation and depressive symptoms by applying marginal structural models (MSM) with machine-learning (ML) algorithms.

methodsWe analyzed data from the 8-15

resultsThe random forest algorithm performed best among all algorithms, showing the highest area under the curve 0.702, 95% confidence interval (CI) 0.686-0.718. In the MSM with the random forest algorithm, workers who changed from standard to non-standard employment were 2.07 times more likely to report suicidal ideation compared to those who maintained standard employment (95% CI 1.16-3.70). A similar trend was observed in the analysis of depressive symptoms.

conclusionsThis study found that a change in employment status could lead to a higher risk of suicidal ideation and depressive symptoms.

Indexed as

DepressionSuicidal IdeationAlgorithmsCausalityEmploymentHumans

Identifiers

PMID38466615
PMCPMC11106614

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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 OpenQuestion graph.