Making Meaning Together:Embodied Narratives in a Case of Severe Autism

Shared understanding is generated between individuals before speech through a language of body movement and non-verbal vocalisation, expression of feeling and interest made in gestures of movement and voice. Human understanding is co-created in these embodied projects, displayed in serially organised expressions with shared timing of reciprocal actions between partners. These develop in narrative events that build over cycles of reciprocal expressive action in a four-part structure shared by all the time-based arts: ‘introduction’, ‘development’, ‘climax’, and ‘conclusion’. Pre-linguistic narrative establishes the foundation of later, linguistic intelligence. Yet, participating in social interactions that give rise to narrative development is a central problem of autism spectrum disorder. In this paper, we examine the rapid growth of narrative meaning-making between a non-verbal young woman with severe autism and her new therapist. Episodes of embodied, shared understanding were enabled through a basic therapeutic mode of reciprocal, creative mirroring of expressive gesture. These developed through reciprocal cycles and as the relationship progressed, complete co-created narratives were formed resulting in shared joy and the mutual interest and trust of companionship. These small, embodied stories enabled moments of co-regulated arousal that the young woman had previous difficulty with. These data provide evidence for an intact capacity for non-verbal narrative meaning-making in autism

Conceptual comparison of constructs as first step in data harmonization: Parental sensitivity, child temperament, and social support as illustrations.

This article presents a strategy for the initial step of data harmonization in Individual Participant Data syntheses, i.e., making decisions as to which measures operationalize the constructs of interest - and which do not. This step is vital in the process of data harmonization, because a study can only be as good as its measures. If the construct validity of the measures is in question, study results are questionable as well. Our proposed strategy for data harmonization consists of three steps. First, a unitary construct is defined based on the existing literature, preferably on the theoretical framework surrounding the construct. Second, the various instruments used to measure the construct are evaluated as operationalizations of this construct, and retained or excluded based on this evaluation. Third, the scores of the included measures are recoded on the same metric. We illustrate the use of this method with three example constructs focal to the Collaboration on Attachment Transmission Synthesis (CATS) study: parental sensitivity, child temperament, and social support. This process description may aid researchers in their data pooling studies, filling a gap in the literature on the first step of data harmonization.•Data harmonization in studies using combined datasets is of vital importance for the validity of the study results.•We have developed and illustrated a strategy on how to define a unitary construct and evaluate whether instruments are operationalizations of this construct as the initial step in the harmonization process.•This strategy is a transferable and reproducible method to apply to the data harmonization process

Conceptual comparison of constructs as first step in data harmonization: Parental sensitivity, child temperament, and social support as illustrations

This article presents a strategy for the initial step of data harmonization in Individual Participant Data syntheses, i.e., making decisions as to which measures operationalize the constructs of interest - and which do not. This step is vital in the process of data harmonization, because a study can only be as good as its measures. If the construct validity of the measures is in question, study results are questionable as well. Our proposed strategy for data harmonization consists of three steps. First, a unitary construct is defined based on the existing literature, preferably on the theoretical framework surrounding the construct. Second, the various instruments used to measure the construct are evaluated as operationalizations of this construct, and retained or excluded based on this evaluation. Third, the scores of the included measures are recoded on the same metric. We illustrate the use of this method with three example constructs focal to the Collaboration on Attachment Transmission Synthesis (CATS) study: parental sensitivity, child temperament, and social support. This process description may aid researchers in their data pooling studies, filling a gap in the literature on the first step of data harmonization. •Data harmonization in studies using combined datasets is of vital importance for the validity of the study results. •We have developed and illustrated a strategy on how to define a unitary construct and evaluate whether instruments are operationalizations of this construct as the initial step in the harmonization process. •This strategy is a transferable and reproducible method to apply to the data harmonization process

Individual participant data meta-analysis to compare EPDS accuracy to detect major depression with and without the self-harm item

Item 10 of the Edinburgh Postnatal Depression Scale (EPDS) is intended to assess thoughts of intentional self-harm but may also elicit concerns about accidental self-harm. It does not specifically address suicide ideation but, nonetheless, is sometimes used as an indicator of suicidality. The 9-item version of the EPDS (EPDS-9), which omits item 10, is sometimes used in research due to concern about positive endorsements of item 10 and necessary follow-up. We assessed the equivalence of total score correlations and screening accuracy to detect major depression using the EPDS-9 versus full EPDS among pregnant and postpartum women. We searched Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, and Web of Science from database inception to October 3, 2018 for studies that administered the EPDS and conducted diagnostic classification for major depression based on a validated semi-structured or fully structured interview among women aged 18 or older during pregnancy or within 12 months of giving birth. We conducted an individual participant data meta-analysis. We calculated Pearson correlations with 95% prediction interval (PI) between EPDS-9 and full EPDS total scores using a random effects model. Bivariate random-effects models were fitted to assess screening accuracy. Equivalence tests were done by comparing the confidence intervals (CIs) around the pooled sensitivity and specificity differences to the equivalence margin of delta = 0.05. Individual participant data were obtained from 41 eligible studies (10,906 participants, 1407 major depression cases). The correlation between EPDS-9 and full EPDS scores was 0.998 (95% PI 0.991, 0.999). For sensitivity, the EPDS-9 and full EPDS were equivalent for cut-offs 7-12 (difference range - 0.02, 0.01) and the equivalence was indeterminate for cut-offs 13-15 (all differences - 0.04). For specificity, the EPDS-9 and full EPDS were equivalent for all cut-offs (difference range 0.00, 0.01). The EPDS-9 performs similarly to the full EPDS and can be used when there are concerns about the implications of administering EPDS item 10