ABSTRACT
The COVID-19 pandemic has posed an ongoing threat to the mental wellbeing of countless individuals worldwide, with healthcare workers at particularly high risk. We developed the STEADY staff wellness program prior to the pandemic based on the available literature and input from stakeholders, guided by the Knowledge-to-Action (KTA) Implementation Science Framework. We quickly adapted the STEADY program for implementation in selected high-need units within Canada's largest trauma hospital during the pandemic's first wave. This brief report describes implementation of the STEADY program, retroactively applying the structure of the Knowledge-to-Action Implementation Science Framework to the practical steps taken. We identified the importance of more frequent, shorter contact with HCWs that occurred in-person, with an emphasis on peer support. A flexible approach with strong support from hospital leadership were key facilitators. Our findings suggest that a flexible approach to practical program implementation, theoretically underpinned in best-practices, can result in an acceptable program that promotes increased HCW wellbeing during a pandemic.
ABSTRACT
AimsExperiential learning, such as simulation-based training, is widely used in health education. Dramatic self-expression adds another layer through enacted perspective taking, and embodied self-exploration of interaction with others, to foster situated learning. We describe the evaluation of an innovative drama-based experiential learning project involving collaboration between multidisciplinary mental healthcare staff and people with lived experience of mental illness. The programme was facilitated at East London NHS Foundation Trust by a theatre company experienced in delivering workshops with service users. A weekly group programme took place online over 8 weeks during the COVID-19 pandemic and included activities of improvisation, embodied enactments and debriefing. The programme led to co-production of a drama piece that was filmed and distributed online. It was hypothesised that the experiential learning might result in individual benefits for all participants, such as improved well-being and increased mutual understanding of each other's experience of mental health care. The project aimed to improve relationships between healthcare disciplines, and between staff and service users. Additionally, aims were to empower service users, and support staff to practice core interpersonal skills. Objectives of the evaluation were to study the impact of the experiential learning, understand participants’ experience, and explore challenges and benefits.MethodsA mixed methods approach was taken to evaluate the programme. Following completion of the project, participants were invited to complete a questionnaire utilising a Likert scale rating of overall satisfaction with the project, perceived benefit and impact on specific domains such as working with others. One-to-one semi-structured interviews were conducted according to a topic-guide, and qualitative data were analysed using open & axial coding for thematic analysis.Results11 participants, including Psychiatrists, Occupational Therapists and current service users, completed the experiential learning and filming. Questionnaire data suggested participants were highly satisfied with the learning and felt it would be valuable to others. Themes include the positive experience of creativity, dismantling of hierarchy, improved empathy, confidence and connection. Potential challenges were digital inequality and lack of dedicated time for professional development.ConclusionA drama-based experiential learning group programme for healthcare staff and service users is a highly beneficial learning experience. Participants describe changes on a personal level as well as improved understanding of others’ perspectives. This form of experiential learning features collaborative working that aligns with principles of co-production and supports the development of interpersonal skills;the findings suggest that drama-based experiential learning is a useful method in health education to complement knowledge acquisition.
ABSTRACT
INTRODUCTION: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. METHODS AND ANALYSIS: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. ETHICS AND DISSEMINATION: Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication.