Exploring MHL can be difficult as it consists of several concepts requiring different measurements. There is variation in how MHL is measured: some measures produce a score indicating a quantitative level of MHL, whilst others do not use score-based data. These issues have been synthesised in a recent review (O'Connor, et al., 2014). Vignettes and mixed methodologies are the two approaches commonly used to explore MHL. For the latter, this includes multiple choice questionnaires, Likert scales, dichotomous questions - or a combination of these (O’Connor, et al., 2014). Vignette methodologies, which describe a hypothetical ‘case study’ character experiencing symptoms of a mental disorder, are the most popular method used (O’Connor, et al., 2014; Swami, et al., 2011). They are used to explore participants’ recognition of a mental health problem, interventions they would recommend and their perceived helpfulness, and actions they take do to help the character in the vignette (Jorm, et al., 1997; Reavley & Jorm, 2011b). Vignettes are used as a proxy for the individual’s own attitudes, beliefs and help- seeking intentions. This can be problematic as it is uncertain how
43 participants’ intentions for a vignette translate to their own help- seeking (Burns & Rapee, 2006). Likewise vignettes are not standardised across all studies (Sai & Furnham, 2013). Vignettes are usually based on medical classifications, but their length and detail may affect findings. Sai & Furnham (2013) found differences in the diagnostic labels assigned to six separate vignettes describing depression and schizophrenia respectively, as well as recommended treatments and how participants would help the person. Also, this methodology does not often produce a ‘score’ indicating level of MHL, making cross-study comparisons difficult (O’Connor, et al., 2014).
Other methods applied to measuring MHL include: identifying true and false symptoms of mental disorders (Lauber, Ajdacic-Gross, Fritschi, Stulz, & Rössler, 2005); rating familiarity of real and fabricated mental disorders (Swami, et al., 2011); and rating awareness of mental disorders and beliefs about aetiology and treatments (Furnham, Cook, & Batey, 2011; Swami, et al., 2011). There have been limited attempts at using developed measures, such as the Mental Health Literacy Questionnaire (Davis, et al., 2008), the Friend in Need Questionnaire (Burns & Rapee, 2006) and
the Questionnaire of Assessment of Mental Health Literacy (Loureiro
et al., 2013). Applying measures allows MHL to be measured on a continuum and aids cross-study comparisons (Reavley, Morgan, & Jorm, 2014). Standardised measures do not eradicate use of vignettes, as participants may be presented with vignette(s) prior to
44
measurements. Reavley et al. (2014) developed six similar scales to explore recognition and intervention/treatment beliefs for six mental disorders; the scales only explore two MHL aspects, and as such do not give an overall picture of MHL. The variation of methodologies could affect the validity of measuring MHL and the generalisation of findings; for example, findings relating to recognition of depression and schizophrenia are variable, potentially due to methodological differences (Sai & Furnham, 2013).
It may be difficult for one self-report measure to capture the whole MHL construct. Through systematically reviewing thirteen studies which developed score-based MHL measures, O’Connor et al. (2014) found the majority assessed a minimal number (one to two) of the seven MHL aspects. Recognition of mental disorders was commonly investigated (N=8 studies), with four publications also looking at attitudes affecting recognition and help-seeking behaviour. The differing methodologies, types of data collected, and variation in aspects measured within each study, call into question the validity of measuring this paradigm (O’Connor, et al., 2014). Several measures may be needed to measure MHL. The Attitudes Toward
Seeking Professional Psychological Help Scale [ATSPPHS] (Fischer &
Turner, 1970) explores attitudes towards seeking professional help for mental health issues. The ATSPPHS has been widely applied in several populations and has evidence supporting its psychometric validity and reliability (Elhai, Schweinle, & Anderson, 2008; Mackenzie, et al., 2014). The measure has twenty-nine items
45 relating to stigma of seeking help, need for help, openness in talking about psychological problems, and personal confidence in professionals (Mackenzie, et al., 2014; Rickwood, et al., 2012). A cross-temporal meta-analysis of 22 North American student- sampled (n=6796) studies which used the ATSPPHS found student’ attitudes towards seeking help have become more negative over time (Mackenzie, et al., 2014). ATSPPHS scores declined by almost one Standard Deviation over a forty-year period. The authors suggest health promotion efforts have led to increased acceptance of the medical model and changed attitudes towards pharmacotherapy, which subsequently may have increased mental health-related stigma. Likewise increased negative attitudes could be related to the Cycle of Avoidance model (Biddle, et al., 2007); young people may be normalising higher levels of mental distress, meaning they may have more negative attitudes towards help sources.
Studies vary in how they question participants’ MHL. Several Australian studies exploring young people’s perceptions of the usefulness of help sources have presented participants with a checklist of interventions (Jorm, Morgan, & Wright, 2008; Reavley & Jorm, 2011b; Reavley, McCann, & Jorm, 2012b), while others have asked open-ended questions (Burns & Rapee, 2006). Checklists allow easier comparison between populations (Reavley, et al., 2014), but using closed questions might lead participants into thinking the vignette character definitely has a mental disorder (Sai
46
& Furnham, 2013), while checklists might prompt participants to consider interventions they would not have thought of without a prompt. Comparing studies can be further confounded by the type of data, e.g. qualitative answers vs. dichotomous measures (O’Connor, et al., 2014).