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Experience of Stress and Coping Mechanism Among Police Officers in South Western Nigeria.

Yetunde Olufisayo John-AkinolaAderonke O AjayiMojisola M Oluwasanu
Published in: International quarterly of community health education (2020)
Prior research on stress among police officers in Nigeria is limited. Most researchers have focused on the predictors of workplace stress among the police officer, and fewer still have examined their coping mechanisms. This study assessed the stress experienced and coping mechanism among police officers in Ibadan Metropolis, Oyo State, Nigeria. The study was a descriptive cross-sectional study, which utilized a pretested self-administered questionnaire. The study population included 342 selected respondents using a multistage sampling technique from police stations in Ibadan North Local Government, Oyo State, Nigeria. The data were analyzed using descriptive statistics, χ2 test, and Fisher's exact test at p = .05. Results revealed that majority (92.5%) of the respondents had poor knowledge of stress with a mean knowledge of 5.4 ± 1.7. Majority (80.1%) of the respondents reported experience of stress such as feeling depressed sometimes at work, while 60.5% said that they usually have headache and body ache. In addition, 36.9% had good coping mechanism and more than half (58.8%) had a fair coping mechanism with a mean coping score of 5.0 ± 3.0. This study showed that knowledge of stressors was poor and respondents perceived that they experienced stress and its symptoms. Strategies such as training using teaching, discussion, and explanation to educate the police officers about stress and its coping mechanism and policy interventions to facilitate the construction of standard stress management centers would be appropriate strategies to reduce stress, increase the knowledge of police officers on stressors, and enhance their coping mechanism.
Keyphrases
  • south africa
  • social support
  • depressive symptoms
  • healthcare
  • stress induced
  • mental health
  • cross sectional
  • machine learning
  • deep learning
  • electronic health record