Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis.

Introduction: Experiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms.

Methods: Applying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019.

Agreement between self-reported and researcher-measured height, weight and blood pressure measurements for online prescription of the combined oral contraceptive pill: an observational study.

Objectives: To compare agreement between self-reported height, weight and blood pressure measurements submitted to an online contraceptive service with researcher-measured values and document strategies used for self-reporting.

Design: An observational study.

Setting: An online sexual health service which provided the combined oral contraceptive pill, free of charge, to users in Southeast London, England.

Lived experiences of transgender forced migrants and their mental health outcomes: systematic review and meta-ethnography.

Background: Owing to multiple, complex and intersecting health inequities, systemic oppression and violence and discrimination in their home countries, some transgender people are forced to migrate to countries that offer them better legal protection and wider social acceptance.

Aims: This review sought to explore and understand the multiple factors that shape the mental health outcomes of transgender forced migrants (TFMs).

Method: We systematically searched nine electronic databases for multidisciplinary literature (PROSPERO ID: CRD42020183062).

Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study.

Objective: This paper evaluates the application of a natural language processing (NLP) model for extracting clinical text referring to interpersonal violence using electronic health records (EHRs) from a large mental healthcare provider.

Design: A multidisciplinary team iteratively developed guidelines for annotating clinical text referring to violence. Keywords were used to generate a dataset which was annotated (ie, classified as affirmed, negated or irrelevant) for: presence of violence, patient status (ie, as perpetrator, witness and/or victim of violence) and violence type (domestic, physical and/or sexual).

Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing.

Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner.

A natural language processing approach for identifying temporal disease onset information from mental healthcare text.

Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively.

Birth, attitudes and placentophagy: a thematic discourse analysis of discussions on UK parenting forums.

Background: The post-partum consumption of the placenta by the mother (placentophagy) has been practiced since the 1970s in the global North and is seemingly increasing in popularity. Maternal placentophagy is not known to have been practiced in any other time period or culture, despite being near-ubiquitous in other placental mammals. An in-depth qualitative exploration as to the reasons for the practice, its increasing popularity and how it is narratively incorporated into discourses surrounding "ideal" natural and medical births are given in this paper.