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Drawing on theoretical principles related to goal pursuit and inference, the present study investigated the extent to which specific message features led to better or worse conversational outcomes of end-of-life discussions between older adults and their adult children. Actor-partner interdependence modeling analysis of longitudinal reports from 66 parent/child dyads revealed that tactical attention to identity, relational, and task goals in conversation predicted change over a 1-year period in advance directive completion, concordance accuracy, and relationship satisfaction and closeness. Quantity features of communication (i.e., number of conversations, number of topics discussed) were not related to the measured outcomes. Routine relationship maintenance and explicit decision making had a positive impact on the outcomes, and underaccommodation, strategic relationship maintenance, avoidance, elaboration, and implicit decision making had a negative impact on the outcomes of end-of-life family talk. The findings provide insight on how to practically improve the quality of end-of-life conversations in families.

AbstractThis study uses the relational turbulence model to examine depressive symptoms in newly married women. It reports data from a pre‐/posttest study in which 152 women completed an online survey during their engagement and 6 months after their wedding. Nearly 12% of the women reported reliable and clinically meaningful increases in depressive symptoms after their wedding. Using panel data analysis, this study found that increases in self uncertainty, partner uncertainty, and relationship uncertainty as well as in partner interference were associated with increases in depressive symptoms. This novel test of the relational turbulence model examined the impact of change in (rather than level of) relational turbulence, and the findings extend previous work by demonstrating how mechanisms of relational turbulence impact mental health.

IntroductionIncreasingly, communities are designing and implementing contextualized approaches to integrated service delivery. Having communities in the lead is critical to successful design and implementation. However, how do researchers work with communities to ensure accurate contextual interpretations of linked administrative data?
Objectives and ApproachPolicyWise for Children & Families has been working with communities to create a framework and assist in the implementation of integrated Community-based Mental Health Service Hubs for youth ages 11-24. The approach used to support these communities included multiple sources of data to highlight the need for youth specific mental health care, integrated service delivery and the opportunity for system transformation. This included, but wasn't limited to, a qualitative environmental scan and individual-level linked administrative data from PolicyWise's Child and Youth Data Lab.
ResultsThe goal of presenting the qualitative and quantitative data summaries was to inform community-led design and implementation of the Mental Health Service Hubs. This presentation describes the constructive interplay of community context, qualitative literature and linked administrative data. Community context and the qualitative environmental scan assisted community members in interpreting the administrative data.
Conclusion/ImplicationsThis applied project provides an example of how community members contextualize information from linked administrative data and environmental scan findings to influence program development. The implications of this is that data can reinforce or reshape what communities know about service use and impact how they proceed with planning.

IntroductionLinked datasets are important resources for research, but linkage errors can lead to incorrect results. For data security and privacy concerns, when linkage of personal identifiers is performed anonymously, it is difficult to assess the quality of the linked dataset. We describe the method used to perform linkage quality.
Objectives and ApproachWe explored how to check the quality of linkages while preserving the privacy of individuals. We also adopted an approach that minimized time and burden on data providers involved in physical verification using randomly-generated appropriate sample sizes.
To validate these linkages, data providers were given random samples of 50 unique records from both linked and unlinked individuals across two other Government programs. Data providers were asked to look at the records associated with those individuals in their original datasets. Three types of linkage results were validated: cross-program linkages, cross-program non-linkages, and within-program linkages. Proportions of false-matches and missed-matches were estimated.
ResultsTwenty data providers checked their samples with two other programs which gave us a sample of 2000 individuals. The linkage process, based on anonymized personal identifiers, resulted in high true positive and high true negative rates. Agreement between human judges and the linkage software was strong. Results of this exercise and other linkage validation examinations provided confidence in the accuracy of the linkage process. With false matches occurring approximately only 3% of the time and virtually no missed-matches occurring, no adjustments were deemed necessary. Although linkage rates were reassuring, the sample sizes used for comparison were small, so it is expected that there would be significant variation associated with this 3% estimate; caution is advised in its use.
Conclusion/ImplicationsProportions of false-matches and missed-matches determine linkage quality which is the base for research when linkages are performed anonymously. A low proportion of false-matches and an absence of missed-matches was an indication of robust linkages.