Development of Measure Yourself Concerns and Wellbeing for informal caregivers of people with cancer – a multicentred study

Purpose: Measure Yourself Concerns and Wellbeing (MYCaW) is a validated person-centred measure of the concerns and wellbeing of people affected by cancer. Research suggests that the concerns of informal caregivers (ICs) are as complex and severely rated as people with cancer, yet MYCaW has only been used to represent cancer patients’ concerns and wellbeing. This paper reports on the development of a new qualitative coding framework for MYCaW to capture the concerns of ICs, to better understand the needs of this group. Methods: This multicentred study involved collection of data from ICs receiving support from two UK cancer support charities (Penny Brohn UK and Cavendish Cancer Care). Qualitative codes were developed through a detailed thematic analysis of ICs’ stated concerns. Results: Thematic analysis of IC questionnaire data identified key themes which were translated into a coding framework with two overarching sections; 1. ‘informal caregiver concerns for self’ and 2. ‘informal caregiver concerns for the person with cancer’. Supercategories with specific accompanying codes were developed for each section. Two further rounds of framework testing across different cohorts allowed for iterative development and refinement of the framework content. Conclusions: This is the first person-centred tool specifically designed for capturing IC’s concerns through their own words. This coding framework will allow for IC data to be analysed using a rigorous and reproducible method, and therefore reported in a standardised way. This may also be of interest to those exploring the needs of ICs of people in other situations

Phenotypic Signatures Arising from Unbalanced Bacterial Growth

Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify “phenotypic signatures” by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains