302
Views
4
CrossRef citations to date
0
Altmetric
Articles

Measuring and predicting mental health literacy for depression

&
Pages 293-311 | Received 21 Aug 2015, Accepted 25 Aug 2015, Published online: 09 Dec 2015
 

Abstract

This study investigated if mental health literacy for depression is a multidimensional construct, and sought to identify its components and to construct empirically derived questionnaire subscales. Following a literature review, items were generated to produce five conceptual subscales. These were administered to 228 respondents (136 females, 92 males), along with others measuring psychological constructs of theoretical relevance, specifically assessing: emotional intelligence, mindfulness, interdependence and independence, rebelliousness, and depressive symptomatology. Principal components analysis of the 38-item pool confirmed a 3-factor solution: (1) Knowledge and belief in psychological strategies for reducing depression, (2) Ability to recognise depression, and (3) Knowledge and beliefs in positive self-care habits for reducing depression. These components comprise the Public Understanding of Depression Questionnaire (PUDQ). Interdependence, emotional intelligence, sex of respondent and previous history of medication for mental health difficulties were found to be independently predictive of scores on the PUDQ subscales, supporting their construct validity, with Cronbach’s Alpha coefficients indicating their internal reliability. The PUDQ is a novel multidimensional tool which can be used in the delivery and assessment of strategies for promoting the understanding of depression amongst at-risk groups.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.