Twenty adolescents with insulin-dependent diabetes mellitus were interviewed to obtain samples of problem situations that create obstacles to dietary adherence. The resulting 57 situations were analyzed using a reliable coding system to determine the presence or absence of 28 stimulus features. A hierarchical cluster analysis was used to identify 10 relatively homogeneous categories of obstacles to dietary adherence: being tempted to stop trying; negative emotional eating; facing forbidden foods; peer interpersonal conflict; competing priorities; eating at school; social events and holidays; food cravings; snacking when home, alone, or bored; and social pressure to eat. Diabetes educators should consider an individual's ability to cope with this array of obstacles to adherence when individualizing treatment. Dietary intervention then can be personalized to address specific situational obstacles.
A scoring key containing adjectives from the Multiple Affect Adjective Check List-Revised (MAACL-R) at or below the Grade 6 reading level (MAACL-R6) was used to rescore data from two nonreferred samples (college students, ns = 52 and 78) and one referred sample of 202 from a community mental health center outpatient clinic. Reliability (measures of internal consistency and test-retest) and validity (correlations with five 5-point self-rating mood scales) were almost as high as those for the MAACL-R, and convergence among the MAACL-R6 scales was not increased. Means for the referred group were significantly higher.
The scoring pattern of the Multiple Affect Adjective Check List-Revised (MAACL-R) under both "simulate good" and "simulate bad" conditions and under two instructional sets, either "simulate" (Sample 1) or "simulate with caution" (Sample 2), was studied in an experiment in which the MAACL-R state form was administered to two groups of college students, each consisting of 160 participants. Each participant was tested twice with both a baseline "actual" trial and either a "simulate bad" or "simulate good" trial with the "actual" and experimental "simulate" trials counterbalanced. Participants in Sample 2, but not Sample 1, received instructions to be "cautious so as not to be detected." As expected, the scoring pattern of the MAACL-R was susceptible to response manipulation in the predicted directions. However, instructions to "simulate with caution" did not produce significantly different scores than did instructions to "simulate" alone. In the second study, Receiver Operating Characteristics (ROC) was used to determine the utility of each MAACL-R scale to detect response simulation. For both "simulate bad" and "simulate good," the PA and PASS scales showed the highest correct classification rates. However, detection of simulating bad was more accurate than determination of simulating good.
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