The Effort-Recovery Model explains high stress levels from discrepancies between job demands, job control and work potential [ 43].
All four models explained significantly high to very high proportions of the variance in coccidioidomycosis rates.
Regarding the level of model fit, PBMS explained high percentages of criterion variance for the intention to use DMT.
The final regression models explained a high degree of variance in predicting isokinetic shoulder flexion (R2 = 0.59) and extension (R2 = 0.67) with a subset of four and five inputs, respectively.
Moreover, two models that often describe spatial abundance occupancy relationships well, the He Gaston and negative binomial models, explained a high amount of the variation in our temporal data.
Both models explained a high proportion of between-subject variation in HRQoL, and both models displayed excellent predictive ability.
Landscape models explained a high percentage of variation (allelic richness: adjusted R = 0.78; pairwise FST: adjusted R = 0.60), and likely apply to other species as well.
For change of pain and change of function, the model that explained higher variance was presented using either the SF-36 or the NASS.
Despite these limitations, the results validate our decision to use different models for high and low peak seasons and are consistent with the existing models to explain high and low peak seasons based on the size of the susceptible population.
In their review, Kushner & Mueser [ 19] described four models explaining the high prevalence of comorbid substance use disorders and other psychiatric disorders; the common factor model, the secondary substance abuse model, the secondary psychopathology model, and the bidirectional model.
