Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action

Abstract

Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a 'care in the community' framework.

Figure 1

Heatmap of behavioural traits’ average and deviation of the mean across games. Collectivity refers to the ratio of contribution in the Collective-Risk Social Dilemma. Cooperation and Optimism refers to the ratio of cooperation and expected cooperation, respectively, in Prisoner’s Dilemma. Trust and Reciprocity refers to the ratio of capital trusted and reciprocated in Trust Game. The left part shows the ratio of individuals without mental conditions: caregivers (professionals and relatives with caregiving tasks) and non-caregivers (relatives without caregiving tasks, friends and others). The right part shows the actions of individuals with mental conditions. Therefore, the number in each cell indicates the ratio of social preferences per subjects in each social dilemma and the color scale shows the deviation of the mean measured in SD units.

Figure 2

(a) Individual contribution over rounds. Evolution of contributions (mean and standard error of the mean) during the game between participants with mental disorder conditions, caregivers and non-caregivers. We can see that all groups behave similarly and in an identical way to a previous experiment run outside the mental health ecosystem. (b) Average individual contribution per round. Average contribution and standard error of the mean in the mental health ecosystem. There are significant differences between participant with MD and the rest of actors, caregivers (t-test, $t=2.107,df=155,p<0.0294$) and non-caregivers (t-test, $t=2.499,df=48,p=0.01588$). Distribution of choices by participants with MD (c), caregivers (d) and non-caregivers (e). The most of participants with MD (43.6%) selected the maximum contribution (4), while the caregivers (46.5%) and non-caregivers (48.9%) mostly selected the fair contribution (2).

Figure 3

(a) Average round of achievement. Round (mean and standard error of the mean) in which the group of six achieved the target. (b) Aggregated contributions per group composition. Contributions (mean and standard error of the mean) in the first and last five-rounds per number of individuals with MD in a group. There are significant differences (t-test p < 0.01) in contributions in the first part of the game. (c) Contributions per group of six. Total group contributions by number of individuals with mental conditions in the group. (d) Gini index of final payoff within groups. Level of inequality in final payoff based on the number of individuals with MD in each group.