Several pilot studies have provided evidence that mindfulness-based intervention is beneficial during pregnancy, yet its effects in mothers during the early parenting period are unknown. The purpose of the present pilot study was to examine the effectiveness of a mindfulness-based intervention in breast-feeding mothers. We developed and tested an 8-week mindfulness-based intervention aimed at improving maternal self-efficacy, mindfulness, self-compassion, satisfaction with life, and subjective happiness, and at reducing psychological distress. A randomized controlled, between-groups design was used with treatment and control groups (n = 26) and pretest and posttest measures. ANCOVA results indicated that, compared to the control group, mothers in the treatment group scored significantly higher on maternal self-efficacy, some dimensions of mindfulness (observing, acting with awareness, non-judging, and non-reactivity), and self-compassion (self-kindness, mindfulness, over-identification, and total self-compassion). In addition, mothers who received the treatment exhibited significantly less anxiety, stress, and psychological distress. The results supported previous research findings about the benefits of mindfulness-based intervention in women from the perinatal and postpartum periods through the early parenting period. Additional research is needed to validate our findings in non-breast-feeding mothers and to examine the intervention's indirect benefits in terms of family relationships and child development.

The 12-item version of the General Health Questionnaire (GHQ-12) has become a popular screening instrument with which to measure general psychological health in different settings. Previous studies into the factorial structure of the GHQ-12 have mainly supported multifactor solutions, and only a few recent works have shown that the GHQ-12 was best represented by a single substantive factor when method effects associated with negatively worded items were considered. Confirmatory factor analysis was applied to compare competing measurement models from previous research, including correlated traits, correlated methods approaches and correlated traits, correlated uniquenesses approaches, to obtain further evidence about the factorial structure of the GHQ-12. This goal was achieved with data from 3,050 participants who completed the GHQ-12 included in the Catalonian Survey of Working Conditions (Catalonian Labor Relations and Quality of Work Department, 2012). The results showed additional evidence that the GHQ-12 has a unidimensional structure after controlling for method effects associated with negatively worded items. Furthermore, we found evidence for our hypothesis about the spurious nature of the 3-factor solution in Graetz's (1991) model after comparing its fit with that found for alternative models resulting from different combinations of the negatively worded items. An implication of our results is that future research about the factor structure of the GHQ-12 should take method effects associated with negative wording into account in order to avoid reaching inaccurate conclusions about its dimensionality.

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As some authors have noticed in fields other than psychology, level of measurement and distributional characteristics of count data are commonly not taken into account, so that they are analysed as normally distributed continuous variables, and therefore some general linear model is applied. In this work, we review a random sample of 457 articles published in the last four years in journals with the highest impact factor in the Journal Citation Reports (JCR Social Sciences Edition) of the Institute for Scientific Information. The goals are to know how often count variables appear in psychological applied research and which data analyses are used when dealing with count response variables. Examination showed that there is a notable presence of count variables, especially in some topics in psychology and a remarkable misapplication of the general linear model. One deals with causes and consequences of this approach to data analysis and suggests the use of specific models to analyse count data.

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