Evidence map›Paper›PMID 37554305›Full record

ArticlePsychology research and behavior management2023

Application of Intelligent Lie Recognition Technology in Laws and Regulations Based on Occupational Mental Health Protection.

Xin Tang

Abstract read
In one paragraph

Article in Psychology research and behavior management, 2023. 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

1 author.

Xin TangSchool of Law, Chongqing University, Chongqing, 400044, People's Republic of China.ORCID 0000-0003-0377-029X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Since the reform and opening up, the social economy has developed rapidly. The competition in the employer market is fierce, which leads leaders to have strict requirements for workers, and workplace stress increases. The blind pursuit of corporate economic benefits has led to the neglect of workers' mental health. Employee retaliation against the corporate occurs frequently. The perfection of the legal system for occupational mental health protection is imminent. Methods: Based on the above questions, this study first introduces the research background, significance, and purpose in the introduction. Second, in the literature review, the current status of research is sorted out, the problems in the existing research are summarized, and the innovation points of this study are highlighted. Then, in the method section, the algorithms and models used here are introduced, including convolutional neural networks, long short-term memory networks, and the design of interview processes. Finally, the results of the questionnaire survey and the experimental test are analyzed. Results: (1) There is further room for optimization of intelligent lie recognition technology. (2) The employee assistance program system can effectively solve the mental health problems of employees. (3) There is a need to expand the legislative mechanism for workers' mental health protection at the legal level. Discussion: This study mainly explores the loopholes of occupational mental health protection under the formulation of laws and regulations. Intelligent lie recognition technology reduces workers' adverse physical and mental health risks due to work. It is dedicated to protecting workers' legitimate rights and interests from the formulation of laws and regulations.

Indexed as

employee assistance program systemintelligent lie recognitionlegal protectionoccupational mental healthworkplace stress

Identifiers

PMID37554305
PMCPMC10404594

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.