Evidence for clinical smoking cessation for adolescents.

OBJECTIVE: This report reviews the evidence that informs the role of health and mental health care providers in addressing youth smoking cessation. DESIGN: Qualitative literature review. RESULTS: Physicians do not consistently screen adolescents for tobacco use and fail to provide recommended cessation advice. Challenges to addressing smoking cessation include the need for procedures to ensure confidentiality and the existence of competing demands to provide other services. Few published studies have specifically addressed the effectiveness of clinical interventions. Interventions that require return visits or follow-up phone contacts are technically difficult to implement in this population. Successful interventions may require resources not available in nonresearch settings. Most studies have used brief clinical intervention as a control condition, making it impossible to evaluate its effectiveness. CONCLUSION: There is little evidence that supports current clinical smoking cessation guidelines for adolescents. More research is needed to develop inexpensive, efficient clinical interventions that can provide youths access to smoking cessation help. Future challenges include reorganizing clinical systems to offer greater counseling by support staff or in electronic formats and to provide effective booster messages and follow-up care in a population that is difficult to track.     

The Embodied Penman: Effector‐Specific Motor–Language Integration During Handwriting

Several studies have illuminated how processing manual action verbs (MaVs) affects the programming or execution of concurrent hand movements. Here, to circumvent key confounds in extant designs, we conducted the first assessment of motor–language integration during handwriting—a task in which linguistic and motoric processes are co‐substantiated. Participants copied MaVs, non‐manual action verbs, and non‐action verbs as we collected measures of motor programming and motor execution. Programming latencies were similar across conditions, but execution was faster for MaVs than for the other categories, regardless of whether word meanings were accessed implicitly or explicitly. In line with the Hand‐Action‐Network Dynamic Language Embodiment (HANDLE) model, such findings suggest that effector‐congruent verbs can prime manual movements even during highly automatized tasks in which motoric and verbal processes are naturally intertwined. Our paradigm opens new avenues for fine‐grained explorations of embodied language processes.     

How help during pregnancy can undermine self-efficacy and increase postpartum intentions to quit

Pregnancy reflects a common experience for women in today's workforce, yet recent data suggest that some women scale back or leave the workforce following childbirth. Considering these effects on women's careers, researchers have sought to understand the underlying dynamics of these decisions. Here, we explore a paradoxical reason for weakened postpartum career attitudes: help that women receive during pregnancy. We integrate stereotype threat and benevolent sexism theories to explain how the effects of help on postpartum intentions to quit may be transmitted through reductions in work self-efficacy. In doing so, we consider the role of perceived impact—or the extent to which help interferes with versus enables women's perceived ability to continue performing their work role. Results of a weekly diary study of 105 pregnant employees suggest that work-interfering help led to decreased self-efficacy for work during the following week. Furthermore, there was an indirect effect of average help received at work during pregnancy on postpartum intentions to quit the workforce through reductions in work self-efficacy that was stronger insofar as help was work-interfering versus work-enabling. Taken together, our results highlight unintended negative consequences that occur when others provide ineffective support to women at work during pregnancy.     

Two ways of learning associations

How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance.