Matches in SemOpenAlex for { <https://semopenalex.org/work/W4214884958> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W4214884958 abstract "This study aims to explore new educational strategies suitable for the mental health education of college students. Big data and artificial intelligence (AI) are combined to evaluate the mental health education of college students in sports majors. First, the research status on the mental health education of college students is introduced. The internet of things (IoT) on mental health education, a structure based on big data and convolutional neural network (CNN), is constructed. Next, the survey design and questionnaire survey are carried out. Finally, the questionnaire data are analyzed and compared with the mental health status under traditional education. The results show that the CNN model has good accuracy and ability to distinguish symptoms, so it can be applied to the existing psychological work in colleges. In the symptom comparison survey, under the traditional education and big data network, the number of college students with mild mental health problems is found to be 158 (84.9%) and 170 (91.4%), respectively. It indicates that the number of college students with moderate mental health problems decreases significantly. In the comparative investigation of the severity of mental problems, the number of students with normal mental health, subhealth, and serious mental health problems under the background of traditional mental health education is 125 (67.2%), 56 (30.1%), and 5 (2.7%), respectively. The mental health status of college students under the influence of big data networks on mental health education is better than that of traditional mental health education. There are 140 students with normal mental health, a year-on-year increase of 16.7%. In the comparative survey of specific mental disorders, students with obsessive-compulsive symptoms under traditional mental health education account for 22.0% of the total sample, having the largest proportion. In the subhealth psychological group under the big data network on mental health education, the number of hostile students decreases by 7, which is the psychological factor with the most obvious improvement. Hence, the proposed path of mental health education is feasible." @default.
- W4214884958 created "2022-03-05" @default.
- W4214884958 creator A5029818365 @default.
- W4214884958 creator A5058816291 @default.
- W4214884958 creator A5062278188 @default.
- W4214884958 creator A5067242485 @default.
- W4214884958 date "2022-03-03" @default.
- W4214884958 modified "2023-10-06" @default.
- W4214884958 title "Exploration and Strategy Analysis of Mental Health Education for Students in Sports Majors in the Era of Artificial Intelligence" @default.
- W4214884958 cites W2072360182 @default.
- W4214884958 cites W2793274185 @default.
- W4214884958 cites W2904663244 @default.
- W4214884958 cites W2922244709 @default.
- W4214884958 cites W2943939821 @default.
- W4214884958 cites W2968578661 @default.
- W4214884958 cites W2972426720 @default.
- W4214884958 cites W2973213154 @default.
- W4214884958 cites W3007172259 @default.
- W4214884958 cites W3012159408 @default.
- W4214884958 cites W3016939326 @default.
- W4214884958 cites W3028011283 @default.
- W4214884958 cites W3049547760 @default.
- W4214884958 cites W3074865674 @default.
- W4214884958 cites W3087762696 @default.
- W4214884958 cites W3091675969 @default.
- W4214884958 cites W3091691410 @default.
- W4214884958 cites W3093424417 @default.
- W4214884958 cites W3105741028 @default.
- W4214884958 cites W3111510044 @default.
- W4214884958 cites W3120271639 @default.
- W4214884958 cites W3122390253 @default.
- W4214884958 cites W3122826593 @default.
- W4214884958 cites W3130020699 @default.
- W4214884958 cites W3131851869 @default.
- W4214884958 cites W3135978089 @default.
- W4214884958 cites W3142778659 @default.
- W4214884958 cites W3151562519 @default.
- W4214884958 cites W3160121812 @default.
- W4214884958 cites W3174132559 @default.
- W4214884958 cites W3192749496 @default.
- W4214884958 cites W3194400295 @default.
- W4214884958 cites W3196538973 @default.
- W4214884958 cites W3197294483 @default.
- W4214884958 cites W3198966822 @default.
- W4214884958 cites W3202329895 @default.
- W4214884958 cites W3204418165 @default.
- W4214884958 cites W3207364811 @default.
- W4214884958 doi "https://doi.org/10.3389/fpsyg.2021.762725" @default.
- W4214884958 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35308079" @default.
- W4214884958 hasPublicationYear "2022" @default.
- W4214884958 type Work @default.
- W4214884958 citedByCount "5" @default.
- W4214884958 countsByYear W42148849582022 @default.
- W4214884958 countsByYear W42148849582023 @default.
- W4214884958 crossrefType "journal-article" @default.
- W4214884958 hasAuthorship W4214884958A5029818365 @default.
- W4214884958 hasAuthorship W4214884958A5058816291 @default.
- W4214884958 hasAuthorship W4214884958A5062278188 @default.
- W4214884958 hasAuthorship W4214884958A5067242485 @default.
- W4214884958 hasBestOaLocation W42148849581 @default.
- W4214884958 hasConcept C118552586 @default.
- W4214884958 hasConcept C124101348 @default.
- W4214884958 hasConcept C134362201 @default.
- W4214884958 hasConcept C145420912 @default.
- W4214884958 hasConcept C15744967 @default.
- W4214884958 hasConcept C41008148 @default.
- W4214884958 hasConcept C509550671 @default.
- W4214884958 hasConcept C71924100 @default.
- W4214884958 hasConcept C75630572 @default.
- W4214884958 hasConcept C75684735 @default.
- W4214884958 hasConceptScore W4214884958C118552586 @default.
- W4214884958 hasConceptScore W4214884958C124101348 @default.
- W4214884958 hasConceptScore W4214884958C134362201 @default.
- W4214884958 hasConceptScore W4214884958C145420912 @default.
- W4214884958 hasConceptScore W4214884958C15744967 @default.
- W4214884958 hasConceptScore W4214884958C41008148 @default.
- W4214884958 hasConceptScore W4214884958C509550671 @default.
- W4214884958 hasConceptScore W4214884958C71924100 @default.
- W4214884958 hasConceptScore W4214884958C75630572 @default.
- W4214884958 hasConceptScore W4214884958C75684735 @default.
- W4214884958 hasLocation W42148849581 @default.
- W4214884958 hasLocation W42148849582 @default.
- W4214884958 hasLocation W42148849583 @default.
- W4214884958 hasLocation W42148849584 @default.
- W4214884958 hasOpenAccess W4214884958 @default.
- W4214884958 hasPrimaryLocation W42148849581 @default.
- W4214884958 hasRelatedWork W1573950854 @default.
- W4214884958 hasRelatedWork W158795003 @default.
- W4214884958 hasRelatedWork W2054737930 @default.
- W4214884958 hasRelatedWork W2135248005 @default.
- W4214884958 hasRelatedWork W2529578884 @default.
- W4214884958 hasRelatedWork W2581220523 @default.
- W4214884958 hasRelatedWork W2899084033 @default.
- W4214884958 hasRelatedWork W2931387495 @default.
- W4214884958 hasRelatedWork W3171911302 @default.
- W4214884958 hasRelatedWork W4237303684 @default.
- W4214884958 hasVolume "12" @default.
- W4214884958 isParatext "false" @default.
- W4214884958 isRetracted "false" @default.
- W4214884958 workType "article" @default.