20212023年上海市心理热线危机来电的时间分布特征

Temporal distribution characteristics of crisis calls to Shanghai mental health hotline from 2021 to 2023

  • 摘要:
    目的 分析上海市心理热线危机来电整体及其8种不同咨询问题的时间分布特征,为提高心理热线管理水平提供依据。
    方法 对 2021年10月—2023年9月上海市心理热线的1 106例危机来电总体及其8种不同问题类型在不同月份、时间段和星期的数量分布进行描述性统计分析,通过χ2检验分析危机来电总体及8种问题类型的数量在不同月份、时间段和星期的数量分布是否存在差异。
    结果 危机来电数量在不同月份(χ2=87.816,P<0.05)、时间段(χ2=161.848,P<0.05)和星期分布(χ2=63.329,P<0.05)之间差异有统计学意义。在月份分布上,来电数量最多为9月,最少为1月;在时间段分布上,来电最多的时段为18:00—20:00,最少的为3:00—5:00;在星期分布上,来电最多的为周六,最少的为周三。在危机来电不同咨询问题类型中,来电数量最多主要为精神疾病类问题,最少为新型冠状病毒感染类问题。
    结论 上海市心理热线危机来电呈现集中的时间分布特征,负责心理热线的相关机构可以根据危机来电时间分布特征优化资源配置。

     

    Abstract:
    Objective To analyze the temporal distribution characteristics of crisis calls and eight major counseling issues to the Shanghai mental health hotline, and to provide recommendations for improving hotline management.
    Methods Descriptive statistics were used to analyze 1 106 crisis calls and the calls of eight major counseling issues to the Shanghai mental health hotline across months, time periods and weeks from October 2021 to September 2023, and the chi-square test was used to analyze whether there was any difference in the distribution of 1 106 crisis calls and the calls of eight major counseling issues across months, time periods and weeks.
    Results There were significant differences in the number of crises calls across different months (χ2=87.816, P<0.05), time periods (χ2=161.848, P<0.05), and weekly distributions (χ2=63.329, P<0.05). The highest number of calls occurred in September, while January had the lowest. The peak call times were between 18:00‒20:00, with the fewest calls occurring between 3:00‒5:00. The day of the week with the highest number of calls was Saturday, while Wednesday had the lowest. Among the different types of counselling issues in crisis calls, the highest number of calls were related to mental disorders, while the fewest calls were related to COVID-19.
    Conclusion Crisis calls to the Shanghai mental health hotline are concentrated at specific times, indicating that relevant organizations should optimize resource allocation based on this time distribution.

     

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