医学背景社交网络构建对大学生HIV防治知信行干预效果

项晶; 刘涛; 陶慧

doi:10.16835/j.cnki.1000-9817.2025300

医学背景社交网络构建对大学生HIV防治知信行干预效果

doi: 10.16835/j.cnki.1000-9817.2025300

项晶¹,
刘涛²,
陶慧^3, ,

1.
南开大学医院妇产科, 天津 300191
2.
南开大学医院预防科
3.
南开大学医院眼耳鼻喉科

基金项目:

天津市性病艾滋病防治协会《2025年青年学生青春健康项目》 TJXAX202501

教育部人文社会科学基金青年项目 19YJC840030

详细信息

作者简介:
项晶(1983-), 女, 湖北黄冈人, 硕士, 主治医师, 主要从事妇科生殖内分泌疾病临床管理、性与生殖健康服务策略与健康促进工作

通讯作者:
陶慧, E-mail: th13116137781@163.com

利益冲突声明 所有作者声明无利益冲突。
中图分类号: G479 TN711 R179
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出版历程
- 收稿日期: 2025-06-24
- 修回日期: 2025-08-07
- 刊出日期: 2025-10-25

Intervention effect of building social networks with medical backgrounds on college students' knowledge, attitudes, and practices in the HIV prevention and treatment

XIANG Jing¹,
LIU Tao²,
TAO Hui^{3
, ,}

1.
Department of Obstetrics and Gynecology, Nankai University Hospital, Tianjin 300191, China

摘要

摘要: 目的评估高校人类免疫缺陷病毒(HIV)防控中具备医学背景的同伴在社交网络中的结构传播优势及其对大学生知识-态度-实践(KAP)水平的干预效果, 为结构化健康传播提供理论支撑与实践路径。方法 2025年4—6月在南开大学采用分层整群随机抽样方法选取367名本科生。采用多模态策略构建学生社交网络, 并以中介中心性≥0.3、特征向量中心性≥P₉₀、担任学生干部≥1年、在课堂或信息互动中被提及≥2次的标准筛选50名同伴节点实施共5轮、每轮5 d的干预, 同伴领袖每日通过微信/QQ群推送5次微信息(知识点、互动话题或视频), 干预前后测量艾滋病防治KAP量表得分, 采用配对t检验和Logistic回归分析干预成效与预测因子。结果干预后艾滋病防治KAP总得分由(65.86±6.58)分提升至(71.27±4.90)分, 差异有统计学意义(t=-12.84, P < 0.01)。医学组中介中心性高于非医学组[(0.51±0.09) (0.34±0.12), t = 3.18, P<0.01], 平均每天传播速率峰值达8.71人, 网络模块度由0.39提升至0.47(ΔQ=0.09, P < 0.01)。结构优势与医学背景交互作用显著预测KAP改善(OR=4.21, 95%CI=1.89~9.38, P < 0.05)。结论专业同伴在高校HIV防治干预中具有显著结构传播优势, 建议结合网络结构和专业背景优化种子节点筛选策略。
- 人免疫缺陷病毒蛋白质类 /
- 健康知识, 态度, 实践 /
- 干预性研究 /
- 回归分析 /
- 学生
Abstract: Objective To evaluate the structural communication advantages of peers with medical background in social networks and their intervention effects on college students' knowledge, attitudes, and practices (KAP) levels in the prevention and control of human immunodeficiency virus (HIV), so as to provide theoretical support and practical pathways for structured health communication. Methods From April to June in 2025, 367 undergraduates were selected by stratified cluster random sampling in Nankai University. A multimodal strategy was employed to construct students' social networks. Fifty peer nodes were selected through a four-step screening process: betweenness centrality ≥0.3, eigenvector centrality ≥P₉₀, serving as student leader for at least one year, being mentioned ≥2 times in classroom interactions and information exchanges, with a total of five rounds, each lasting five days. Peer leaders daily pushed five Micromessage (knowledge points, interactive topics, or videos) through WeChat/QQ groups. KAP scale scores for AIDS prevention and treatment were measured before and after the intervention, and intervention effectiveness and predictive factors were analyzed using paired t-tests and Logistic regression analysis. Results After the intervention, the total KAP score for AIDS prevention and treatment increased from (65.86±6.58) to (71.27±4.90) (t=-12.84, P < 0.01). The intermediary centrality (0.51±0.09) in the medical group was significantly higher than the non-medical group (0.34±0.12) (t=3.18, P < 0.01), with an average daily peak transmission rate of 8.71 people, and the network modularity increased from 0.39 to 0.47 (ΔQ=0.09, P < 0.01). The interaction between structural advantages and medical background significantly predicted KAP improvement (OR=4.21, 95%CI=1.89-9.38, P < 0.05). Conclusions Professional peers have significant structural communication advantages in HIV prevention and treatment interventions in colleges and universities. It is recommended to optimize sub-node screening strategies by combining network structure and professional background.
- Human immunodeficiency virus proteins /
- Health knowledge, attitudes, practice /
- Intervention studies /
- Regression analysis /
- Students
注释:

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图 1 干预期间高校HIV信息传播的拟合趋势及演化阶段划分

注：圆点为观测值，黑实线为拟合曲线。

Figure 1. S-curve fitting of HIV information diffusion during the intervention and its evolutionary stages

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表 1 不同组别大学生艾滋病防治KAP水平分布比较

Table 1. Comparison of KAP level of HIV prevention and treatment among different groups of college students

组别	选项	人数	低	中	高	χ²值	Cramer's V值
性别	女	181	4(2.2)	71(39.2)	106(58.6)	23.06^*	0.25
	男	186	21(11.3)	96(51.6)	69(37.1)
年龄/岁	18~20	161	10(6.2)	73(45.3)	78(48.4)	6.71	0.10
	21~23	123	6(4.9)	51(41.5)	66(53.7)
	24~26	83	9(10.8)	43(51.8)	31(37.3)
年级	大一	93	6(6.5)	45(48.4)	42(45.2)	3.37	0.07
	大二	91	7(7.7)	44(48.4)	40(44.0)
	大三	95	7(7.4)	36(37.9)	52(54.7)
	大四	88	5(5.7)	42(47.7)	41(46.6)
专业	医学	78	0	10(12.8)	68(87.2)	65.45^*	0.42
	非医学	289	25(8.7)	157(54.3)	107(37.0)
月消费水平/元	< 1 000	83	13(15.7)	42(50.6)	28(33.7)	29.70^*	0.28
	1 000~2 000	106	6(5.7)	59(55.7)	41(38.7)
	2 001~3 000	88	2(2.3)	31(35.2)	55(62.5)
	>3 000	90	4(4.4)	35(38.9)	51(56.7)
班干部	否	178	14(7.9)	79(44.4)	85(47.8)	0.66	0.04
	是	189	11(5.8)	88(46.6)	90(47.6)
Cb中心度分组	低	205	17(8.3)	92(44.9)	96(46.8)	1.61	0.07
	高	162	8(4.9)	75(46.3)	79(48.8)
标注次数分组/次	0~1	171	12(7.0)	73(42.7)	86(50.3)	1.03	0.05
	≥2	196	13(6.6)	94(48.0)	89(45.4)
合计		367	25(6.8)	167(45.5)	175(47.7)
注： ()内数字为构成比/%；Cramer's V为χ²效应量指标，用于衡量分类变量间的关联强度；Cb中心度分组按Cb是否≥0.3分为高/低两组，用于比较网络结构位置与KAP水平的关系；标注次数分组按个体在同伴问卷提名与课堂观察中被提及的次数划分，用于衡量其在群体中的显著性；^*P < 0.05。

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表 2 大学生艾滋病防治KAP改善预测因子的二元Logistic回归模型(n=367)

Table 2. Binary Logistic regression analysis of factors associated with KAP improvement in HIV/AIDS prevention and treatment among college students (n=367)

自变量	选项	B值	标准误	Z值	P值	OR值(95%CI)
性别	男	-0.11	0.23	-0.50	0.62	0.89(0.57~1.39)
月消费水平/元	2 001~3 000	0.04	0.33	0.11	0.91	1.04(0.54~1.99)
	< 1 000	0.20	0.33	0.62	0.54	1.22(0.64~2.33)
	>3 000	0.14	0.32	0.45	0.66	1.15(0.62~2.15)
专业	医学	0.31	0.32	0.98	0.33	1.37(0.73~2.56)
结构优势节点	Cb≥0.3且被提名≥2次	1.10	0.29	3.83	< 0.01	3.00(1.71~5.27)
交互作用	结构优势×医学专业	0.47	0.67	0.70	< 0.01	4.21(1.89~9.38)
注：性别以男、月消费水平以1 000~2 000元、专业以非医学专业为参照；结构优势节点中满足Cb≥0.3且被提名≥2次赋值1，否则为0；交互作用仅“结构优势×医学专业节点”中两者均赋值1时为1，其余为0。

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