Outdoor nature-based play in early learning and childcare centres: Identifying the determinants of implementation using causal loop diagrams and social network analysis

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

“…McGlashan et al 24 "for population health problems, the insight of central variables can aid intervention planning by understanding their role in the system"; "insight from network analysis can aid community groups in intervention design by considering a variable's position in the network" Savi et al 50 "the calculation of the metrics combined with the properties of the network enables the identification of potential strategies that may guide policy recommendations for better control of malaria" Smith et al 52 "leverage points can be identified through topological analysis of the system map structure"; "these leverage points become the focus of interventions that could promote equitable use of urban blue spaces" Uleman et al 8 "high centrality for modifiable risk factors such as social relationships and physical activity (…) suggest that they may be promising leverage points for interventions" Zucca et al 55 "rather than trying to intervene across the whole system of nature-based early learning and childcare centres delivery it may be most rewarding to invest available resources in subsections of the system, focusing on the small number of leverage points identified in this study as being the most important/applicable to the context of the any given early learning and childcare centres practice"…”

“…In seeking alternative methods, network analysis 35 has gained momentum as an accepted practice to quantitatively identify leverage points based on CLDs 8 , 23 – 30 , 36 – 55 —for example in public health 24 , 38 , 45 , 50 , 52 , 53 , 55 . Here, 22 of the 29 papers that we identified that adopt this practice were published in 2021 (4 papers 23 , 25 , 37 , 50 ), 2022 (8 papers 27 , 28 , 43 – 45 , 47 , 49 , 52 ), and 2023 (10 papers to date 26 , 30 , 36 , 38 , 39 , 41 , 42 , 46 , 53 , 55 ). Network analysis seemingly has the potential to mathematically analyse a CLD, which in this context can be considered a network of factors, to systematically find which factors represent leverage points.…”

“…In current practice, the results obtained from applying betweenness- and closeness centrality, among other network analysis metrics, to CLDs have been utilised to inform policy recommendations. Examples of this are given in Table 1 8 , 24 , 42 , 45 , 50 , 52 , 55 . In this context, factors with the highest rankings on betweenness- and/or closeness centrality, among other network analysis metrics, are presented as places in the system for which effective intervention is likely to have the largest impact on specified system outcomes.…”

“…In seeking alternative methods, network analysis 35 has gained momentum as an accepted practice to quantitatively identify leverage points based on CLDs 8,[23][24][25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] -for example in public health 24,38,45,50,52,53,55 . Here, 22 of the 29 papers that we identified that adopt this practice were published in 2021 (4 papers 23,25,37,50 ), 2022 (8 papers 27,28,[43][44][45]47,49,52 ), and 2023 (10 papers to date …”

“…The overarching approach taken in using network analysis to identify leverage points based on CLDs is to translate the CLD's factors and arrows into an adjacency matrix, after which network analysis metrics can be employed. Metrics commonly applied in combination with CLDs are betweenness centrality 8,[23][24][25][26][27]29,[38][39][40][41][42][43][44][45]48,[50][51][52][53][54][55] and closeness centrality 8,23,26,29,39,42,43,50,52,53 . The factor with the highest betweenness centrality lies on the highest number of shortest causal chains between pairs of other factors in the CLD 58 , hypothesised to indicate that it is an important mediator 8,24,26,29,38,42 .…”