数智时代工作紧张人群阈下抑郁的影响因素：基于机器学习的证据

doi:10.3724/SP.J.1041.2025.2001

摘要/Abstract

摘要：

阈下抑郁作为抑郁症的前兆, 对抑郁风险有重要警示作用。本研究结合扎根理论和机器学习方法, 构建了中国工作紧张人群阈下抑郁影响因素的机器学习模型, 并对各因素之间的关联结构, 因素词频、职业差异和年代变化进行了分析。发现：(1)工作紧张人群阈下抑郁的表征包含5个主范畴, 其中意志减弱词频占比最高; 影响因素包含8个主范畴, 工作因素、评价适应和自主选择词频占比分列前三位。(2) 8类影响因素均与阈下抑郁表征紧密关联; 基于关联规则的网络分析结果显示出“自我认知”、“行为自由”、“环境适应”和“一般社交”副范畴的重要作用。(3)医护人员的躯体因素与其他职业具有显著差异。(4) 2011至2023年间, 工作因素呈上升趋势, 人际因素呈下降趋势。(5)基于BERT的机器学习模型可以用于判别工作紧张人群阈下抑郁的影响因素, 且判别结果能够对工作紧张人群的抑郁风险做出较好预测, 采用XGBoost算法的预测准确率为81.58%, 其中对阈下抑郁组预测的F1分数为0.90, AUC值为0.93。本文拓展了阈下抑郁在工作紧张人群中的研究, 为工作紧张人群阈下抑郁的识别与防治提供了新视角。

关键词: 阈下抑郁, 工作紧张, 机器学习

Abstract:

Depression is one of the most common psychological problems, and subthreshold depression, as a precursor to its occurrence, plays a vital warning role in the prevention and treatment of depression. However, there is currently a lack of in-depth analysis of the representations and influencing factors of subthreshold depression in people with occupational stress in China (SDPOSC). This study integrated grounded-theory research with machine learning methods to explore the manifestations and influencing factors of subthreshold depression among Chinese working population under stress. BERT technology was utilized to construct a discriminant model for identifying the factors influencing subthreshold depression within this population, and the model's effectiveness was subsequently studied and confirmed.

This research is composed of two studies. The first study involved the analysis of network texts harvested through web crawling, employing grounded theory for coding to establish a framework of factors influencing subthreshold depression in individuals under stress. The correlation structure between influencing factors and their representations was further explored, along with association rule analysis between influencing factors. Word frequency analysis and occupational difference tests were then conducted to analyze the characteristics of the influencing factors. Mann-Kendall test was subsequently applied to analyze the development trend of influencing factors. Based on the analysis of online text, the second study constructed a machine learning model using BERT technology. the influencing factors of subthreshold depression are distinguished and the effectiveness of the model is subsequently confirmed.

Results showed that (1) Manifestations of subthreshold depression in people with occupational stress have five categories, with weakened willpower the highest frequency of expression and daily behavioral changes the lowest frequency. (2) Main influencing factors consist of eight categories, with work factors, evaluation adaptation, and autonomous selection the highest frequency; and stress events the lowest. (3) Eight types of influencing factors were closely related to subthreshold depression symptoms, with stressful event the best single predictor. Network analysis based on association rules revealed that "self-awareness, " "behavioral freedom, " "environmental adaptation, " and "general social interaction" are the most important subcategories. (4) Healthcare professionals had a significant difference in somatic factors compared to other professions, identified by a difference test of word frequency distribution. (5) Words related to work factors has shown an upward trend from 2011 to 2023, while those related to interpersonal factors have shown a downward trend. (6) A BERT-based machine learning model is obtained and it works in identifying influencing factors of subthreshold depression in populations experiencing work-related stress, in particular, the XGBoost algorithm achieved a prediction accuracy of 81.58%, with particularly strong performance in subthreshold depression detection (F1-score = 0.90, AUC = 0.93).

This study provided an in-depth analysis of the representations and influencing factors of SDPOSC, enriching the localization research of subthreshold depression from an empirical perspective. Furthermore, a machine learning model by BERT can be utilized in subsequent research. The study of SDPOSC can help identify their depression risk and has important theoretical and practical significance for the prevention and treatment of SDPOSC.

Key words: subthreshold depression, occupational stress, machine learning

中图分类号:

R395

邓丽芳, 裴蓓, 高天艾. (2025). 数智时代工作紧张人群阈下抑郁的影响因素：基于机器学习的证据. 心理学报, 57(11), 2001-2021.

DENG Lifang, PEI Bei, GAO Tian’ai. (2025). The factors affecting subthreshold depression for people with occupational stress in the era of digital intelligence: Machine learning-based evidence. Acta Psychologica Sinica, 57(11), 2001-2021.

图/表 20

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主范畴	副范畴	概念	关键词举例
负性思维	自我贬低	自我否定、自我反责	妄自菲薄、自责、愧疚
	过度思考	偏执强迫、过度分析	患得患失、想不通、钻牛角尖
	消极期盼	消极预测、期盼缺乏	生不如死、没盼头、没指望
情绪异常	不良情绪积累	悲伤低落、敏感担忧	难过、情绪低落、担心、焦躁
情绪异常	积极情绪缺失	兴趣下降、愉悦缺失	失去兴趣、没意思、不开心
意志减弱	疲乏拖延	疲乏无力、任务拖延	累、疲劳、倦怠、不想做
	驱动力锐减	目标缺失、动机减弱	无所事事、自暴自弃、咸鱼
	注意力下降	注意力涣散、记忆困难	昏昏沉沉、发呆、忘、记不清
日常行为变化	自伤自杀倾向	自伤倾向、自杀意念	自残、伤口、想死、自杀念头
	成瘾行为	物质成瘾、网络代偿	过度消费、酗酒、买醉
	人际回避	社交活动减少、人际关系疏远	社恐、处不好、屏蔽
生理习惯变化	饮食与体重	饮食习惯变化、体重变化	暴饮暴食、没食欲、暴瘦
生理习惯变化	睡眠问题	睡眠模式改变、睡眠质量不佳	失眠、睡不着、多梦

主范畴	副范畴	概念	关键词举例
负性思维	自我贬低	自我否定、自我反责	妄自菲薄、自责、愧疚
	过度思考	偏执强迫、过度分析	患得患失、想不通、钻牛角尖
	消极期盼	消极预测、期盼缺乏	生不如死、没盼头、没指望
情绪异常	不良情绪积累	悲伤低落、敏感担忧	难过、情绪低落、担心、焦躁
情绪异常	积极情绪缺失	兴趣下降、愉悦缺失	失去兴趣、没意思、不开心
意志减弱	疲乏拖延	疲乏无力、任务拖延	累、疲劳、倦怠、不想做
	驱动力锐减	目标缺失、动机减弱	无所事事、自暴自弃、咸鱼
	注意力下降	注意力涣散、记忆困难	昏昏沉沉、发呆、忘、记不清
日常行为变化	自伤自杀倾向	自伤倾向、自杀意念	自残、伤口、想死、自杀念头
	成瘾行为	物质成瘾、网络代偿	过度消费、酗酒、买醉
	人际回避	社交活动减少、人际关系疏远	社恐、处不好、屏蔽
生理习惯变化	饮食与体重	饮食习惯变化、体重变化	暴饮暴食、没食欲、暴瘦
生理习惯变化	睡眠问题	睡眠模式改变、睡眠质量不佳	失眠、睡不着、多梦

主范畴	副范畴	概念	关键词举例
躯体疾病	躯体异常	肢体疼痛、躯体不适	腰疼、头疼脑热、呕吐
	生理疾病	心脑血管疾病、癌症、呼吸系统疾病、其他疾病	咽炎、肿瘤、高血压
评价适应	自我认知	自我评价、自尊水平	自知、自信、优越感
评价适应	环境适应	环境评估、应对环境	时代、趋势、适应
情绪管理	情绪内化	稳定、压制	情绪稳定、镇定、控制情绪
	情绪抒发	表达、宣泄	释放、发泄、爆发
	情绪重构	分散、重构	脱离、释怀、疏通
自主选择	权利保障	规则限制、权益保障、经济物质基础	法定、维权、社会保障
	行为自由	机会选择、发展规划、生活自主	自主、选择权、机会
人格因素	外向性	活跃、合群、乐观/防御、被动、悲观	热情、亲切、积极性/清高、腼腆、悲观
	利他性	友善、诚信/自私、虚伪	善良、真诚、负责/刻薄、不负责、伪装
	谨慎性	严谨、细致、负责/敷衍、马虎、放纵	谨慎、细心/草率、放纵
	果敢性	决断、坚韧、勇敢/犹豫、脆弱、怯懦	果断、坚强/软弱、犹豫不决
	机敏性	机智、变通/愚笨、浅薄	聪慧、灵活/无知、守旧
	随和性	豁达、淡泊/狭隘、竞争	心胸宽广、不争不抢/心胸狭隘、暴躁
工作因素	工作要求	岗位胜任、时间压力、任务负荷、工作制度	执行力、工作效率、工作量
	工作条件	办公环境、待遇与前景、通勤条件	办公室、薪资、班车
	组织人文环境	团队氛围、人文关怀、职场人际	归属感、照顾新人、指导
应激事件	个体应激事件	生存变故、经济损失、家庭变故	车祸、破产、负债、丧偶
应激事件	社会环境应激事件	自然灾害、社会性灾难	火灾、地震、疫情、战争
人际因素	一般社交	平常往来、网络社交	朋友、同学、网友、社交圈
人际因素	亲密往来	恋人关系、亲属关系	男/女朋友、父母、一家人

主范畴	副范畴	概念	关键词举例
躯体疾病	躯体异常	肢体疼痛、躯体不适	腰疼、头疼脑热、呕吐
	生理疾病	心脑血管疾病、癌症、呼吸系统疾病、其他疾病	咽炎、肿瘤、高血压
评价适应	自我认知	自我评价、自尊水平	自知、自信、优越感
评价适应	环境适应	环境评估、应对环境	时代、趋势、适应
情绪管理	情绪内化	稳定、压制	情绪稳定、镇定、控制情绪
	情绪抒发	表达、宣泄	释放、发泄、爆发
	情绪重构	分散、重构	脱离、释怀、疏通
自主选择	权利保障	规则限制、权益保障、经济物质基础	法定、维权、社会保障
	行为自由	机会选择、发展规划、生活自主	自主、选择权、机会
人格因素	外向性	活跃、合群、乐观/防御、被动、悲观	热情、亲切、积极性/清高、腼腆、悲观
	利他性	友善、诚信/自私、虚伪	善良、真诚、负责/刻薄、不负责、伪装
	谨慎性	严谨、细致、负责/敷衍、马虎、放纵	谨慎、细心/草率、放纵
	果敢性	决断、坚韧、勇敢/犹豫、脆弱、怯懦	果断、坚强/软弱、犹豫不决
	机敏性	机智、变通/愚笨、浅薄	聪慧、灵活/无知、守旧
	随和性	豁达、淡泊/狭隘、竞争	心胸宽广、不争不抢/心胸狭隘、暴躁
工作因素	工作要求	岗位胜任、时间压力、任务负荷、工作制度	执行力、工作效率、工作量
	工作条件	办公环境、待遇与前景、通勤条件	办公室、薪资、班车
	组织人文环境	团队氛围、人文关怀、职场人际	归属感、照顾新人、指导
应激事件	个体应激事件	生存变故、经济损失、家庭变故	车祸、破产、负债、丧偶
应激事件	社会环境应激事件	自然灾害、社会性灾难	火灾、地震、疫情、战争
人际因素	一般社交	平常往来、网络社交	朋友、同学、网友、社交圈
人际因素	亲密往来	恋人关系、亲属关系	男/女朋友、父母、一家人

前项	后项	支持度	置信度	提升度
应激事件	阈下抑郁表征	0.154	0.976	1.138
情绪管理	阈下抑郁表征	0.558	0.965	1.125
躯体疾病	阈下抑郁表征	0.371	0.952	1.110
人格因素	阈下抑郁表征	0.666	0.951	1.109
人际因素	阈下抑郁表征	0.739	0.935	1.090
自主选择	阈下抑郁表征	0.886	0.908	1.059
评价适应	阈下抑郁表征	0.896	0.906	1.056
工作因素	阈下抑郁表征	0.918	0.897	1.046