Media Use Pattern with Respect to Mental Health in COVID-19: A Dataset from India

The purpose of the study is to fill a void how media as a whole (including social and electronic media) has been impacted the mental health of Indian people in terms of growing anxiety and mental well-being.Thus the research questions addressed in the study are- 1. Is there any relationship between media use pattern with mental Well-being and Anxiety? 2. How the demographic attributes do associated with mental Well-being and Anxiety? Data collection: The researchers here conducted a web based cross sectional survey to assess the psychological impact of COVID-19 on the Indian public. The Data was gathered after three weeks of commencement of lock down in India from 426 respondents through snowball sampling. Media use pattern: There were ten structured validated items regarding the information and news about COVID-19 perspective. Depending upon the hour of exposer to the media, the respondents were categorised in to three group subsequently High (≥9 hrs.), Medium (7-8 hrs.) and Low (≤6 hrs.)Media user. Mental Well-Being Scale: Warwick-Edinburgh Mental Well-Being Scale (NHS Health Scotland, University of Warwick and University of Edinburgh, 2006) was used to collect data about the status of mental well-being. It was a 5 point Likert type scale having 14 items. The summated score of the respondent is used as mental well-being score. It is also classified as high (Score>56), medium (Score 47-56) and low (score<47) mental well-being. Mental Anxiety Scale Beck Anxiety Inventory (BAI) Scale (Beck et al., 1988) was applied to collect the information regarding anxiety level. It was a 4 point Likert scale having 21 items. The summated score of the respondent is also used as mental anxiety score. It is also classified as high (Score>35), medium (Score 22-35 ) and low (score<22) mental anxiety. Data Analysis: The demographic characteristics here include age (young, middle & old); gender (male & female); habitat (rural, urban municipality & metropolitan city); and educational qualification (undergraduate or below, postgraduate & above postgraduate). The data showed that there is significant negative relationship exists between of respondent’s media use and mental wellbeing status, and significant positive relationship exists between media use of respondents’ mental anxiety. Any researcher may use the data to test the association between demographic variables with media user pattern, mental well-being and mental anxiety. The data might be helpful for the psychologist and policy makers to make decision about mental health condition during COVID-19 crisis both in and outside of India.本研究旨在填补现有研究空白，系统探究全媒体（含社交媒介与电子媒介）对印度民众心理健康的影响，聚焦焦虑水平攀升与心理福祉变化维度。据此，本研究拟解答如下两项核心研究问题：1. 民众的媒介使用模式与心理福祉、焦虑水平之间是否存在显著关联？2. 人口统计学特征与心理福祉、焦虑水平存在何种关联？ 数据采集：本研究团队开展基于网络的横断面调查，以评估新冠疫情（COVID-19）对印度民众的心理影响。数据采集于印度全国封城启动三周后，通过滚雪球抽样法共回收426份有效问卷。 媒介使用模式：本研究设置10项经过信效度检验的结构化条目，用于评估受访者对新冠疫情相关资讯与新闻的接触情况。根据每日媒介接触时长，将受访者划分为三大组别：高时长使用者（每日≥9小时）、中时长使用者（每日7-8小时）与低时长使用者（每日≤6小时）。 心理福祉量表：本研究采用沃里克-爱丁堡心理福祉量表（Warwick-Edinburgh Mental Well-Being Scale，NHS Health Scotland、华威大学与爱丁堡大学，2006年编制）采集受访者的心理福祉状态数据。该量表为14条目5点李克特式量表，以受访者的总分作为心理福祉得分，并据此划分为高福祉组（得分＞56）、中福祉组（得分47-56）与低福祉组（得分＜47）。 焦虑量表：本研究采用贝克焦虑量表（Beck Anxiety Inventory，BAI，Beck等，1988年编制）采集受访者的焦虑水平数据。该量表为21条目4点李克特式量表，以受访者的总分作为焦虑得分，并据此划分为高焦虑组（得分＞35）、中焦虑组（得分22-35）与低焦虑组（得分＜22）。 数据分析：本研究纳入的人口统计学特征包括年龄（青年、中年、老年）、性别（男性、女性）、居住地域（农村、城镇、大都市）以及受教育程度（本科及以下、硕士研究生及以上）。数据分析结果显示，受访者的媒介使用时长与心理福祉状态呈显著负相关，与焦虑水平呈显著正相关。本数据集可供研究者用于检验人口统计学变量与媒介使用模式、心理福祉及焦虑水平之间的关联，亦可帮助心理学家与政策制定者为印度国内外新冠疫情期间的民众心理健康干预决策提供参考依据。