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by Gabriel Ott, Yannik Schaubelt, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff
Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.Health professionals witness pain and suffering when they care for sick people and their families. Compassion is a necessary quality in their work as it combines the will to help, alleviate suffering and promote the well-being of both the people they are attending and the professionals themselves. The aim of the study was to design and evaluate the psychometric properties of the Capacity for Compassion Scale (CCS).
A quantitative, descriptive and cross-sectional study was carried out to evaluate the psychometric properties of the scale (reliability, temporal stability, content validity, criterion validity and construct validity).
The study was carried out in two phases: pilot study and final validation. The data were collected between April and May 2022. The sample was selected by convenience sampling and was made up of a total of 264 participants, 59 in the pilot phase and 205 in the final validation.
The Capacity for Compassion Scale has been shown to have good psychometric properties in relation to reliability, temporal stability, and content, criterion, and construct validity. Factor analysis showed that there were four subdimensions of the scale: motivation/commitment, presence, shared humanity and self-compassion. The results also indicate that compassionate ability is significantly correlated with age and work experience.
The Capacity for Compassion Scale shows adequate psychometric properties. This instrument measures the compassion capacity of health professionals, which is a valuable discovery for new lines of research in this field.
Through this scale, low levels of capacity for compassion can be detected that negatively influence the quality of care provided by health professionals. The Capacity for Compassion Scale can therefore contribute to the identification of needs and promote training around compassion for health professionals.
No patient or public contribution.
Compassion in health professionals has positive effects on improving the quality of care, the satisfaction of professionals and the work environment. There are compassion cultivation programmes whose validity has been proven for the development of the dimensions of compassion. There is no specific instrument that measures capacity for compassion in healthcare professionals.
A scale is designed to measure capacity for compassion in health professionals. This is the only such scale available up until now. The scale measures four dimensions of compassion: motivation/commitment, presence, shared humanity and self-compassion.
The development of specific programmes that can increase the compassion of health professionals with all the benefits that this can bring to health care is encouraged. It will be possible to analyse the effects of training programmes on the cultivation of compassion.
The study explores the experiences of women with low-risk pregnancies and no complications who planned a home birth.
A cross-sectional study was conducted using an online questionnaire.
The questionnaire included socio-demographic, obstetric and perinatal variables. Birth satisfaction was evaluated via the Spanish version of the childbirth experience questionnaire. The study group comprised home-birthing women in Catalonia, Spain. Data were collected from 1 January 2019 to 31 December 2021. Statistical analysis was performed using SPSS.
A total of 236 women responded. They reported generally positive experiences, with professional support and involvement being the most highly rated dimensions. Better childbirth experiences were associated with labour lasting less than 12 h, no perineal injuries, no intrapartum transfers to hospital, euthocic delivery and the presence of a midwife.
Women's positive home birth experiences were linked to active participation and midwife support. Multiparous women felt safer. Medical interventions, especially transfers to hospitals, reduced satisfaction, highlighting the need for improved care during home births.
Home births should be included among the birthplace options offered by public health services, given the extremely positive feedback reported by women who gave birth at home.
Home birth is not an option offered under Catalonia's public health system only as a private service. The experience of home-birthing women is unknown. This study shows a very positive birth experience due to greater participation and midwife support. The results help stakeholders assess home birth's public health inclusion and understand valued factors, supporting home-birthing women.
The study followed the STROBE checklist guidelines for cross-sectional studies.
Women who planned a home birth participated in the pilot test to validate the instrument, and their contributions were collected by the lead researcher. The questionnaire gathered the participants' email addresses, and a commitment was made to disseminate the study's results through this means.
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