Investigating patient acceptability of stratified medicine for schizophrenia : a mixed methods study

Background Health services have advocated a stratified medicine approach in mental health, but little is known about whether service users would accept this approach. Aims To explore service users’ views of the acceptability of stratified medicine for treatment-resistant schizophrenia compared to the traditional “trial-and-error” approach. Methods A mixed methods observational study that explored questionnaire responses on acceptability and whether these responses were affected by demographic or clinical variables. We also investigated whether treatment responsiveness or experience of invasive tests (brain scans and blood tests) affected participants’ responses. Questionnaire generated qualitative data were analyzed thematically. Participants (N108) were aged 18–65, had a diagnosis of schizophrenia, and were adherent to antipsychotic medication. Results Acceptability of a stratified approach was high, even after participants had experienced invasive tests. Most rated it as safer (62% vs 43%; P < .01 [CI: −1.69 to 2.08]), less risky (77% vs 44%; P < .01 [CI: −1.75 to 1.10]), and less painful (90% vs 73%; P < 0.01 [CI: −0.84 to 0.5]) and this was not affected by treatment responsiveness or test experience. Although not statistically significant, treatment nonresponders were more willing to undergo invasive tests. Qualitatively, all participants raised concerns about the risks, discomfort, and potential side effects associated with the invasive tests. Conclusions Service users were positive about a stratified approach for choosing treatments but were wary of devolving clinical decisions to purely data-driven algorithms. These results reinforce the value of service user perspectives in the development and evaluation of novel treatment approaches

Ketamine treatment for depression : qualitative study exploring patient views

Background Ketamine is a new and promising treatment for depression but comes with challenges to implement because of its potential for abuse. Aims We sought the views of patients to inform policy and practical decisions about the clinical use of ketamine before large-scale roll-out is considered. Method This qualitative study used three focus groups and three validation sessions from 14 patients with prior diagnoses of depression but no experience of ketamine treatment. Focus groups explored their views about clinical use of ketamine and the best way for ketamine to be administered and monitored. The qualitative data were analysed by three service-user researchers using thematic analysis. Results Five themes were generated: changing public perceptions, risks, monitoring, privacy and data protection, and practical aspects. Participants were conscious of the stigma attached to ketamine as a street drug and wanted better public education, and evidence on the safety of ketamine after long-term use. They felt that monitoring was required to provide evidence for ketamine's safe use and administration, but there were concerns about the misuse of this information. Practical aspects included discussions about treatment duration, administration and accessibility (for example who would receive it, under what criteria and how). Conclusions Patients are enthusiastic about ketamine treatment but need more information before national roll-out. The wider societal impact of ketamine treatment also needs to be considered and patients need to be part of any future roll-out to ensure its success

Identifying schizophrenia stigma on Twitter : a proof of principle model using service user supervised machine learning

Stigma has negative effects on people with mental health problems by making them less likely to seek help. We develop a proof of principle service user supervised machine learning pipeline to identify stigmatising tweets reliably and understand the prevalence of public schizophrenia stigma on Twitter. A service user group advised on the machine learning model evaluation metric (fewest false negatives) and features for machine learning. We collected 13,313 public tweets on schizophrenia between January and May 2018. Two service user researchers manually identified stigma in 746 English tweets; 80% were used to train eight models, and 20% for testing. The two models with fewest false negatives were compared in two service user validation exercises, and the best model used to classify all extracted public English tweets. Tweets classed as stigmatising by service users were more negative in sentiment (t (744) = 12.02, p < 0.001 [95% CI: 0.196–0.273]). Our linear Support Vector Machine was the best performing model with fewest false negatives and higher service user validation. This model identified public stigma in 47% of English tweets (n5,676) which were more negative in sentiment (t (12,143) = 64.38, p < 0.001 [95% CI: 0.29–0.31]). Machine learning can identify stigmatising tweets at large scale, with service user involvement. Given the prevalence of stigma, there is an urgent need for education and online campaigns to reduce it. Machine learning can provide a real time metric on their success

sj-docx-1-jad-10.1177_10870547241229096 – Supplemental material for Loneliness in Young People with ADHD: A Systematic Review and Meta-Analysis

Supplemental material, sj-docx-1-jad-10.1177_10870547241229096 for Loneliness in Young People with ADHD: A Systematic Review and Meta-Analysis by Angelina Jong, Clarissa Mary Odoi, Jennifer Lau and Matthew J.Hollocks in Journal of Attention Disorders</p