Diabetes duration, perceived comfort with self-management and glycaemic control in adolescents with type 1 diabetes: A cross-sectional study.

AIMS: Evidence is lacking on whether diabetes duration is associated with type 1 diabetes (T1D) self-management during late adolescence before transfer from paediatric to adult care. We examined associations of diabetes duration with dimensions of perceived comfort with diabetes self-management (self-efficacy, transition readiness, diabetes distress) and glycaemic control in late adolescence. METHODS: Using a cross-sectional design, we conducted a secondary analysis of baseline data of adolescents (ages 16-17 years) with T1D followed at paediatric diabetes academic hospitals in Montreal and enrolled in the Group Education Trial to Improve Transition (GET-IT-T1D). Participants completed validated questionnaires on self-efficacy (Self-Efficacy for Diabetes Self-Management Measure [SEDM], score 1 to 10), diabetes distress and transition readiness, as well as a haemoglobin (HbA1c) capillary blood test. Our primary outcome was self-efficacy. We examined associations of diabetes duration with self-efficacy, diabetes distress, transition readiness and HbA1c using linear and logistic regression models adjusted for sex, socioeconomic status, insulin pump use, glucose sensor use and psychiatric comorbidity. RESULTS: Of 203 adolescents with T1D, mean diabetes duration (SD) was 7.57 (4.44) years. Mean SEDM score was 6.83 (SD 1.62). Diabetes duration was not associated with self-efficacy, diabetes distress or transition readiness. Each additional year of diabetes duration was associated with 0.11% (95% CI, 0.05 to 0.16) higher HbA1c. CONCLUSIONS: Although diabetes duration is not associated with dimensions of perceived comfort with diabetes self-management, adolescents with longer diabetes duration are at risk for higher HbA1c and may need additional support to improve glycaemic control before transition to adult care.

Direct evidence for a dual process model of deductive inference.

In 2 experiments, we tested a strong version of a dual process theory of conditional inference (cf. Verschueren et al., 2005a, 2005b) that assumes that most reasoners have 2 strategies available, the choice of which is determined by situational variables, cognitive capacity, and metacognitive control. The statistical strategy evaluates inferences probabilistically, accepting those with high conditional probability. The counterexample strategy rejects inferences when a counterexample shows the inference to be invalid. To discriminate strategy use, we presented reasoners with conditional statements (if p, then q) and explicit statistical information about the relative frequency of the probability of p/q (50% vs. 90%). A statistical strategy would accept the more probable inferences more frequently, whereas the counterexample one would reject both. In Experiment 1, reasoners under time pressure used the statistical strategy more, but switched to the counterexample strategy when time constraints were removed; the former took less time than the latter. These data are consistent with the hypothesis that the statistical strategy is the default heuristic. Under a free-time condition, reasoners preferred the counterexample strategy and kept it when put under time pressure. Thus, it is not simply a lack of capacity that produces a statistical strategy; instead, it seems that time pressure disrupts the ability to make good metacognitive choices. In line with this conclusion, in a 2nd experiment, we measured reasoners' confidence in their performance; those under time pressure were less confident in the statistical than the counterexample strategy and more likely to switch strategies under free-time conditions.

More evidence for a dual-process model of conditional reasoning.

Many studies have shown that the deductive inferences that people make have global properties that reflect the statistical information implicit in the premises. This suggests that such reasoning can be explained by a single, underlying probabilistic model. In contrast, the dual process model of conditional reasoning (Verschueren, Schaeken, & d'Ydewalle, 2005b) proposes that people can use either a logical, counterexample-based strategy or a probabilistic one. In two studies, we presented reasoners with sequences of affirmation-of-the-consequent inferences that differed with respect to the statistical properties of the premises, either explicitly or implicitly. As predicted by the dual-process model, an analysis of individual response patterns showed the presence of two distinct strategies, with use of the counterexample strategy being associated with higher levels of abstract-reasoning competence. Use of the counterexample strategy was facilitated by the explicit presentation of counterexample information. In a further study, we then examined explicitly probabilistic inferences. This study showed that although most reasoners made statistically appropriate inferences, the ability to make more-accurate inferences was associated with higher levels of abstract reasoning competence. These results show that deductive inferential reasoning cannot be explained by a single, unitary process and that any analysis of reasoning must consider individual differences in strategy use.

Conditional reasoning, frequency of counterexamples, and the effect of response modality.

Geiger and Oberauer (2007) found that when asked to reason with conditionals, people are very sensitive to information about the relative frequency of exceptions to conditional rules and quite insensitive to the relative number of disabling conditions. They asked participants to rate their degree of certainty in a conclusion. In the following studies, we investigated the possibility that this kind of response encourages a more probabilistic mode of processing compared with the usual dichotomous response. In Study 1, participants were given a variant of the problems used by Geiger and Oberauer with either the same scaled response format or a dichotomous categorical response. The results with the scaled response were identical to those of Geiger and Oberauer. However, the results with the categorical response presented a very different profile. In Study 2, we presented similar problems using only frequency information, followed by a set of abstract conditional reasoning problems. The participants who performed better on the abstract problems showed a significantly different response profile than those who did worse on the abstract problems in the categorical response condition. No such difference was observed in the scaled response condition. These results show that response modality strongly affects the way in which information is processed in otherwise identical inferential problems and they are consistent with the idea that scaled responses promote a probabilistic mode of processing.