LA CIENCIA Y LA CREENCIA POPULAR

8.6.1 Direct extensions

The current findings suggest a number of directions for future research with respect to (i) the nature and (ii) the outcomes of participation. Each is discussed in turn below.

(i) Nature of participation. The studies in this thesis require replication in other MHISGs in order to assess their reliability. In particular, there is a need to investigate if the differing conceptions of recovery between higher- and lower-engaged users are evident in other MHISGs, and if so to undertake follow-up studies to examine the nature of this difference. For example, studies should determine the extent to which the difference in conception of recovery is explicitly acknowledged by users in an MHISG, or whether the current findings are a function of some other important difference between users.

The empirical studies in this thesis focused on frequency of posts. For pragmatic reasons, a wide range of other participatory styles were not investigated. Developing a better

understanding of other participation styles, including those identified by the systematic review in Chapter 2 may have promising applications (e.g., in the optimisation of the dissemination of health information). Further research is also required to investigate if there are other distinct participation styles not identified to date, and if so, to investigate their potential role in the delivery of tailored health promotion strategies.

(ii) Outcomes of participation. Studies are needed to examine a user’s mental health and related outcomes as a function of their interactions within an MHISG. The Tripartite Model of MHISG Participation could provide a framework for the design of such investigations. It is neither feasible nor ecologically valid to randomly allocate users to groups in which they are

instructed to participate using different styles. However, it may be informative to track who is interacting with whom and correlate these occurrences with outcomes of participation, such as empowerment, hope, symptoms, stigma or attitudes towards seeking professional help. Such outcomes may be measured by very brief surveys following user interactions, with effects adjusted for initial status recorded at the time of registration. It would not be necessary to measure all of these outcomes in a single survey or a single person. Other important outcomes could be measured passively. For example the activity of users, such as click-

throughs on links referring users to other professional sources of help, could be tracked online.

Furthermore, optimising MHISG service delivery will require systematic testing of the effects of various modifications to MHISGs to determine what works best and for whom. With respect to the modifications suggested in the Implications section (8.4) above (welcome messages and skills training for users), it should be noted that the desired mental health and behavioural outcomes may differ as a function of different sub-groups of users. For example, a successful manipulation of the welcome messages may increase the engagement of users with high awareness characteristics, whereas success in the case of low-awareness users may increase their professional help-seeking behaviour through referral information. Similarly, it might be hypothesised that a skills training intervention for high-engaged users will yield positive effects on personal recovery through the helper-therapy principle [44] with improvements to positive self-identity [45, 46], whereas low-engaged users who interact with the trained users, may experience greater improvements in hope, empowerment and willingness to seek help [47]. Finally, there is potential to test the effect of identifying and responding to ISG users

employing automated methods such as those that detect symptoms from text [48] and provide automatic tailored feedback in response to users.

8.6.2 Broader implications for future research

As the granularity in the focus on the nature of participation in an online health community is refined, the conception of the community as an intervention in its own right diminishes and a conception of it as a lens through which to explore and influence health behaviour increases, paralleling our notion of face-to-face communities. However, from the perspective of a research enterprise, the online context presents a strong advantage for investigating health behaviour over the off-line context in that the data for all users is recorded by default. The ability to quantify and detect health behaviour and attitudes in this context thus provides a powerful tool to conduct research on topics that are difficult to study in offline behaviour. For

example, using Facebook social network data, a recent study has documented the spread of ‘emotional contagion’ through users of the website following the experimental manipulation of the proportion of positive and negative sentiment content in users’ newsfeeds [49]. Using the same principle, it may be possible to investigate the spread of other psychological phenomena and associated behaviours such as stigma and attitudes towards mental health help-seeking through social networks such as MHISGs. However, a major limitation of this approach is that the data, in its natural language format, must be processed before it can be used to infer these outcomes accurately. This barrier may be addressed in part by undertaking research which employs machine-learning techniques, such as those in the current thesis [4] to detect the phenomena of interest in the content of user interactions.