Acknowledging religion in cognitive behavioural therapy: the effect on alliance, treatment expectations and credibility in a video-vignette study

Objectives: Developing mental health services which are accessible and acceptable to those from minority backgrounds continues to be a priority. In the United Kingdom, individuals who identify with a religion are underrepresented in Talking Therapies services as compared to those with no religion. This necessitates an understanding of how therapy is perceived. This online study explored the impact of explicitly acknowledging religion on anticipated alliance, treatment credibility and expectations of therapy in a non-clinical sample of British Muslims. Methods: A video-vignette experimental design was used in which participants who self-reported as either high or low in religiosity were randomly allocated to receiving information about cognitive behavioural therapy either with or without an explicit mention of religion as a value in the therapeutic process. Results: One hundred twenty-nine British Muslim adults aged 18–70+ years from various ethnic backgrounds participated in the study. Between-subjects ANOVAs showed that scores on the perceived credibility of therapy and treatment expectations were significantly higher when religion was explicitly mentioned by the ‘therapist’, but that acknowledging religion did not impact upon anticipated alliance. Conclusions: These findings suggest that mentioning religion as a value to be considered in therapy has some positive impacts upon how therapy is perceived by British Muslims. Although video vignettes do not provide insight into the complexity of actual therapeutic encounters, acknowledging religion in mental health services more broadly remains an important consideration for improving equity of access and may bear relevance to other minoritized groups

Findability of UK health datasets available for research: a mixed methods study

Objective How health researchers find secondary data to analyse is unclear. We sought to describe the approaches that UK organisations take to help researchers find data and to assess the findability of health data that are available for research.Methods We surveyed established organisations about how they make data findable. We derived measures of findability based on the first element of the FAIR principles (Findable, Accessible, Interoperable, Reproducible). We applied these to 13 UK health datasets and measured their findability via two major internet search engines in 2018 and repeated in 2021.Results Among 12 survey respondents, 11 indicated that they made metadata publicly available. Respondents said internet presence was important for findability, but that this needed improvement. In 2018, 8 out of 13 datasets were listed in the top 100 search results of 10 searches repeated on both search engines, while the remaining 5 were found one click away from those search results. In 2021, this had reduced to seven datasets directly listed and one dataset one click away. In 2021, Google Dataset Search had become available, which listed 3 of the 13 datasets within the top 100 search results.Discussion Measuring findability via online search engines is one method for evaluating efforts to improve findability. Findability could perhaps be improved with catalogues that have greater inclusion of datasets, field-level metadata and persistent identifiers.Conclusion UK organisations recognised the importance of the internet for finding data for research. However, health datasets available for research were no more findable in 2021 than in 2018

Quantifying the longitudinal value of healthcare record collections for pharmacoepidemiology

We introduce an information score for longitudinal healthcare record data, specifically in the monitoring of chronic conditions. The score is designed to capture the value of different observation patterns in terms of shaping and testing clinical epidemiological hypotheses. The score is first developed for the simple case where equally spaced observations are most informative, then extended to a more context-specific version where the optimal density of observations can be elicited. It can be interpreted as a measure of the average quantity of information provided by each observation in an individual’s time course, where information is lost whenever the observation density deviates from a defined optimal density