Community advisory boards (CABs) are a promising approach for strengthening patient and partner voices in community health center (CHC) evidence-based decision-making. This paper aims to describe how CHCs used CABs during the COVID-19 pandemic to improve the reach of testing among populations experiencing health disparities and identify transferable lessons for future implementation.

This mixed methods study integrates brief quantitative surveys of community engagement (N = 20) and one-on-one qualitative interviews (N = 13) of staff and community partners engaged in CHC CABs with a cost analysis and qualitative feedback from CHC staff participating in an online learning community (N = 17).

Community partners and staff engaged in the CHC CABs reported high ratings of engagement, with all mean ratings of community engagement principles above a 4 (“very good” or “often”) out of 5. Qualitative findings provided a more in-depth understanding of experiences serving on the CHC CAB and highlighted how engagement principles such as trust and mutual respect were reflected in CAB practices. We developed a CHC CAB toolkit with strategies for governance and prioritization, cost estimates to ensure sustainment, guidance on integrating quality improvement expertise, testimonies from community members on the benefits of joining, and template agendas and facilitator training to ensure meeting success.

In alignment with the Translational Science Benefits Model, this study expands research impact through comprehensive mixed methods measurement of community engagement and by transforming findings into an action-orientated guide for CHCs to implement CABs to guide evidence-based decision-making for community and public health impact.

To provide a bespoke development program for foundation year (FY) trainees on rotation at CNWL NHS trust.

To increase interest in foundation trainees in choosing Psychiatry as a career.

Specific: 100% self-reported satisfaction with the FY development program by April 2024.

50% increase from baseline of self-reported interest in choosing psychiatry as a specialty by April 2024.

We designed an online, 12 session teaching program for each 4 month cohort of foundation year doctors on rotation at St Charles Hospital, CNWL. We collect data at baseline, after each teaching session and at exit via online questionnaires. These are reviewed at PDSA meetings (including nominees from foundation cohort) by team quarterly. First cohort started in May 2023, we are currently in our 3rd cohort of this project. Each cohort has approximately 15 Foundation year Trainees. Our curricula integrates principles of Co-Design: 2 of the 12 teaching slot topics are voted by each cohort of foundation trainees. Co-Production: 2 of the 12 slots are always for a carer, service user or expert by experience. Bespoke: Each teacher is provided a written guide outlining the training grade of foundation trainees and highlights the needs for transferrable skills as FY trainees may not pursue psychiatry as a career. Quality Improvement: iterative learning from each cohort, with robust data collection methods (dedicated time set aside for feedback completion) and regular reviews by team. Teachers are canvassed via trust emails, trainee Whatsapp groups and patient liaison services at CNWL.

Cohort 1= no data collected. Data collection methods required improvement.

Cohort 2= data collection of 3 responses (23% completion rate). Data collected insufficient. Data collection method improved.

Cohort 3= data collection of 13 responses (87% completion rate).

Self-reported satisfaction with training program: 95.3% Interest in choosing psychiatry as a specialty: 30.4% at baseline to 76.2% at time of submission.

This Foundation Year Development initiative provides a well-liked, bespoke and innovative approach to train foundation year doctors on placement at NHS trusts.

The majority of Foundation doctors (some surveys show 60%) are undecided on their specialty during foundation training and this is a unique opportunity to increase recruitment into psychiatry.

We identify a set of essential recent advances in climate change research with high policy relevance, across natural and social sciences: (1) looming inevitability and implications of overshooting the 1.5°C warming limit, (2) urgent need for a rapid and managed fossil fuel phase-out, (3) challenges for scaling carbon dioxide removal, (4) uncertainties regarding the future contribution of natural carbon sinks, (5) intertwinedness of the crises of biodiversity loss and climate change, (6) compound events, (7) mountain glacier loss, (8) human immobility in the face of climate risks, (9) adaptation justice, and (10) just transitions in food systems.

The Intergovernmental Panel on Climate Change Assessment Reports provides the scientific foundation for international climate negotiations and constitutes an unmatched resource for researchers. However, the assessment cycles take multiple years. As a contribution to cross- and interdisciplinary understanding of climate change across diverse research communities, we have streamlined an annual process to identify and synthesize significant research advances. We collected input from experts on various fields using an online questionnaire and prioritized a set of 10 key research insights with high policy relevance. This year, we focus on: (1) the looming overshoot of the 1.5°C warming limit, (2) the urgency of fossil fuel phase-out, (3) challenges to scale-up carbon dioxide removal, (4) uncertainties regarding future natural carbon sinks, (5) the need for joint governance of biodiversity loss and climate change, (6) advances in understanding compound events, (7) accelerated mountain glacier loss, (8) human immobility amidst climate risks, (9) adaptation justice, and (10) just transitions in food systems. We present a succinct account of these insights, reflect on their policy implications, and offer an integrated set of policy-relevant messages. This science synthesis and science communication effort is also the basis for a policy report contributing to elevate climate science every year in time for the United Nations Climate Change Conference.

We highlight recent and policy-relevant advances in climate change research – with input from more than 200 experts.

Certain neuropsychiatric symptoms (NPS), namely apathy, depression and anxiety demonstrated great value in predicting dementia progression representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing.

To investigate the association between automatically extracted speech features and NPS in early-stage dementia patients.

Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. Presence of NPS was assessed by the Neuropsychiatric Inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.

Different speech variables seem to be associated with specific neuropsychiatric symptoms of dementia; apathy correlates with temporal aspects, anxiety with voice quality and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy and depression scores.

Different NPS seem to be characterized by distinct speech features which in turn were easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS. This could have great implications for future clinical trials.

No significant relationships.

Retraction pockets are collapsed segments of the tympanic membrane. Their formation is associated with a disruption in middle-ear ventilation pathways, leading to the loss of organised collagen of the tympanic membrane. There are several approaches in treating retraction pockets, which aim either to improve ventilation or repair the retraction pockets themselves.

A review of the literature regarding the classification and associated management of retraction pockets was conducted, using Medline and Cochrane Library databases, with the addition of our institution's experience in undertaking novel techniques.

All the current classification systems fail to guide treatment or prognosticate the disease. Commonly, an initial conservative approach is suggested for early stages, with no clear indication regarding when a surgical solution should be offered. There are conflicting data concerning the effectiveness of these treatments in terms of disease prevention, recurrence and hearing outcomes.

Minimally invasive approaches utilising novel techniques may be key to reducing peri-operative morbidity and improving the overall patient experience.

Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders.

Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.

Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality—and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores.

Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.