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To improve induction to medical seclusion reviews (MSRs) for Resident Doctors (RDs) rotating into psychiatry in Derbyshire Healthcare NHS Foundation Trust (DHCFT).
Concerns were previously raised by rotating RDs in DHCFT regarding induction to seclusion and feeling unprepared for leading MSRs. Seclusion, as restrictive practice with potential large impacts on our patients, is closely audited both nationally and locally as per the Mental Health Act Code of Practice. Many RDs are not psychiatry trainees and are therefore unfamiliar with the process. Nursing staff within the trust had also reported concerns regarding safety awareness of RDs during MSRs particularly soon after newly rotating into the trust.
Methods:
We developed a video of a mock scenario encompassing all aspects of a MSR including timing and structure of reviews based on both local guidelines and the Mental Health Act Code of Practice. This video was recorded within a seclusion suite to demonstrate the layout. This was shown to current RDs 3 months into their rotation. A survey wascompleted on previous experience of seclusion, confidence of completing seclusion reviews, whether had been previously inducted on seclusion and preparedness to complete MSRs prior to viewing the video. Results were compared with a follow-up survey after viewing the video assessing its impact.
Results:
Of the 29 respondents to the initial survey; 79% (n=23) reported having no previous induction to seclusion. Respondents were a mixture of Foundation Doctors, GP trainees, Psychiatric Core trainees and Psychiatric Higher Specialty trainees. Mean confidence completing seclusion reviews was 2.66/5. 93% (n=27) of respondents stated they were not prepared enough to complete a MSR after induction. 22 reponses were received to the follow-up survey. Mean confidence completing seclusion reviews increased to 4.23/5. 100% of respondents stated a video on seclusion in induction would be helpful.
Conclusion:
This video has demonstrated a novel, relevant and sustainable approach to delivering induction for RDs surrounding a unique aspect of psychiatry which had previously been insufficient in preparing newly rotating RDs for completing seclusion reviews. This video is being further shared with East Midlands School of Psychiatry so that it can contribute to shared learning across other trusts within the region.
The National Institute for Health and Care Excellence (NICE) in England introduced early value assessments (EVAs) as an evidence-based method of accelerating access to promising health technologies that could address unmet needs and contribute to the National Health Service’s Long Term Plan. However, there are currently no published works considering differences and commonalities in methods used between Assessment Reports for EVAs.
Methods
This rapid scoping review included all completed EVAs published on the NICE website up to 23 July 2024. One reviewer screened potentially relevant records for eligibility, checked by a second reviewer. Pairs of independent reviewers extracted information on the methods used in included EVAs using a prepiloted form; these were checked for accuracy. Data were described in graphical or tabular format with an accompanying narrative summary.
Results
In total, seventeen EVA Reports of sixteen EVAs were included in this scoping review. Five Reports did not specify how many reviewers undertook screening, whereas five did not report data extraction methods. Five EVAs planned to conduct meta-analyses, nine planned narrative syntheses, and seven planned narrative summaries. Eleven conceptual decision models were presented, with available evidence used to construct cost-utility analyses (N = 5); cost-effectiveness analyses (CEAs; N = 4); a mix of CEAs and cost-consequence analyses (CCA; N = 2); one CCA; and one cost-comparison.
Conclusion
Future EVA Reports should enhance the transparency of the methods used. Furthermore, EVAs could provide opportunities for the adoption of innovative methodological approaches and more flexible communication between EVA authors and key stakeholders, including patients and clinicians, companies, and NICE.
Aerosol-cloud interactions contribute significant uncertainty to modern climate model predictions. Analysis of complex observed aerosol-cloud parameter relationships is a crucial piece of reducing this uncertainty. Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. These observations consist of flights over the Western North Atlantic Ocean, providing a large repository of data including aerosol, meteorological, and microphysical conditions in and out of clouds. We investigate this dataset using principal component analysis (PCA), a linear dimensionality reduction technique, and an autoencoder, a deep learning non-linear dimensionality reduction technique. We find that we can reduce the dimensionality of the parameter space by more than a factor of 2 and verify that the deep learning method outperforms a PCA baseline by two orders of magnitude. Analysis in the low dimensional space of both these techniques reveals two consistent physically interpretable regimes—a low pollution regime and an in-cloud regime. Through this work, we show that unsupervised machine learning techniques can learn useful information from in-situ atmospheric observations and provide interpretable results of low-dimensional variability.