Archives of Public Health (Jan 2022)

Training types associated with knowledge and experience in public health workers

  • Zui Narita,
  • Yoshio Yamanouchi,
  • Kazuo Mishima,
  • Yoko Kamio,
  • Naoko Ayabe,
  • Ryoko Kakei,
  • Yoshiharu Kim

DOI
https://doi.org/10.1186/s13690-022-00788-4
Journal volume & issue
Vol. 80, no. 1
pp. 1 – 6

Abstract

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Abstract Background Training non-specialist workers in mental healthcare improves knowledge, attitude, confidence, and recognition of mental illnesses. However, still little information is available on which type of mental health training is important in the improvement of these capacities. Methods We studied web-based survey data of 495 public health workers to examine training types associated with knowledge and experience in supporting individuals with mental illness. Multivariable logistic regression analysis was conducted to evaluate the association between a lack of knowledge and experience (outcome) and mental health training (exposure). We fitted three regression models. Model 1 evaluated unadjusted associations. Model 2 adjusted for age and sex. Model 3 adjusted for age, sex, years of experience, mental health full-time worker status, and community population. Bias-corrected and accelerated bootstrap confidence intervals (CIs) were used. Results For all training types, the association between a lack of knowledge and experience and mental health training attenuated as the model developed. In Model 3, a lack of knowledge and experience was significantly associated with training in specific illness (OR, 0.54; 95% CI, 0.32–0.93) and screening and assessment (OR, 0.63; 95% CI, 0.39–0.99). Non-significant results were produced for training in counseling, psychosocial support, collaborative work, and law and regulation in Model 3. Conclusions We believe that the present study provides meaningful information that training in specific illness and screening and assessment may lead to knowledge and experience of public health workers. Further studies should employ a longitudinal design and validated measurements.

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