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This corresponds with the findings of Gill, Hinrichsen and DiGiuseppe [[ 23] for in-home and out-of-home services], who showed that need variables explain more variance in service utilisation than predisposing or enabling variables speaking in terms of the Andersen model.
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We used variance partitioning to determine the amount of variance in ecosystem service supply explained by the 12 stand-level forest attributes and four environmental factors (location, soil depth, pH and slope) (Fig. 1).
We conducted a variance partitioning analysis to compare the variance in ecosystem services explained by forest attributes and environmental factors.
The shared variance between both groups of variables was usually considerable, explaining on average 20% [0 49%] of the variance in ecosystem services (Supplementary Table 3a).
Forest attributes exclusively explained 23% on average [3 59%] of the variance in ecosystem services compared with 17% [1 38%] for the environmental factors.
Despite the variance in expectation of service provider, there was a preference for all PN services to be delivered by the NHS in the UK.
Stratified analyses revealed that the model explained 18%and16%6% of the variance in dental care service use in KDTH and UST respectively.
We started with an empty, intercept-only model to test the null hypothesis that state-level variance in maternal health services utilization is zero and to assess if our data justify the decision to assess random effects at the state level.
Does this variance in reaction help to promulgate the notion that the Times' is politically biased?
The intra-class correlation in the empty model for antenatal, delivery and postnatal care by skilled provider indicated that 31%, 12%, and 25% of the total variance in use of the services was attributable to the differences across kebeles (Table 3).
Like the sum of the same conditions, it explains variance in selected measures of health service use and health status, in some instances making a contribution beyond that of the summed measure.
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