'I didn't realise they had such a key role.' Impact of medical education curriculum change on medical student interactions with nurses: a qualitative exploratory study of student perceptions

Interprofessional teamwork between healthcare professionals is integral to the delivery of safe high-quality patient care in all settings. Recent reforms of medical education curricula incorporate specific educational opportunities that aim to foster successful interprofessional collaboration and teamwork. The aim of this study was to explore the impact of curriculum reform on medical students’ perceptions of their interactions and team-working with nurses. We gathered data from 12 semi-structured individual narrative interviews with a purposive sample of male (n = 6) and female (n = 6) medical students from fourth year (n = 6 following an integrated curriculum) and fifth year (n = 6 following a traditional curriculum). Data were subject to narrative analysis which was undertaken using NVivo software. Overall, there was no notable difference in the responses of the participants on the traditional and integrated curricula about their interactions and team work with nurses. However, the introduction of an integrated medical curriculum was viewed positively but a lack of interprofessional education with nursing students, removal of a nursing placement and shorter clinical placements were perceived as lost opportunities for the development of educationally beneficial relationships. The participants reported that nurses play a number of roles in clinical practice which underpin patient safety including being medical educators who provide a valuable source of support for medical students. The participants highlighted different factors that could hinder or foster effective working relationships such as a lack of understanding of nurses’ different professional roles and mutual respect. Medical education needs to provide students with more structured opportunities to work with and learn from nurses in clinical practice. Further research could explore how to foster positive relationships between medical students and nurses

A Global Health Data Divide

Health data are a valuable resource for driving innovation and research. Unfortunately, the global distribution of health data is uneven and leaves entire continents behind. Improving the availability of data representing the biggest health needs must be a core priority for fostering a research, development, and innovation ecosystem

A Global Health Data Divide

Health data are a valuable resource for driving innovation and research. Unfortunately, the global distribution of health data is uneven and leaves entire continents behind. Improving the availability of data representing the biggest health needs must be a core priority for fostering a research, development, and innovation ecosystem

Emotional socialisation and burnout in medicine and the role of medical educators

The authors highlight the importance of exploring the role that nurses and other healthcare professionals play in supporting medical students and doctors to develop their emotional competence

Revealing transparency gaps in publicly available Covid-19 datasets used for medical artificial intelligence development:a systematic review

Background: Throughout the Covid-19 pandemic artificial intelligence (AI) models were developed in response to significant resource constraints affecting healthcare systems. Previous systematic reviews demonstrate that healthcare datasets often have significant limitations, contributing to bias in any AI health technologies they are used to develop. This systematic review aimed to characterise the composition and reporting of datasets created throughout the Covid-19 pandemic, and highlight key deficiencies which could affect downstream AI models.Methods: A systematic search of MEDLINE identified articles describing datasets used for AI health technology development. Studies were screened for eligibility, and datasets collated for analysis. Google Dataset Search was used to identify additional datasets. After deduplication and exclusion of datasets not related to Covid-19 or those not containing data relating to individual humans, dataset documentation was assessed for the completeness of metadata reporting, their composition, the means of data access and any restrictions, ethical considerations, and other factors.Findings: 192 datasets were analysed. Metadata were often incomplete or absent. Only 48% of datasets’ documentation described the country where data originated, 43% reported the age of individuals included, and under 25% reported sex, gender, race, ethnicity or any other attributes. Most datasets provided no information on data labelling, ethical review, or consent for data sharing. Many datasets reproduced data from other datasets, sometimes without linking to the original source. We found multiple cases where paediatric chest X-ray images from prior to the Covid-19 pandemic were reproduced in datasets without this being acknowledged. Interpretation: This review highlights substantial deficiencies in the documentation of many Covid-19 datasets. It is imperative to balance data availability with data quality in future health emergencies, or else we risk developing biased AI health technologies which do more harm than good.Funding: This review was funded by The NHS AI Lab and The Health Foundation, and supported by the National Institute for Health and Care Research (AI_HI200014).<br/

Revealing transparency gaps in publicly available Covid-19 datasets used for medical artificial intelligence development - a systematic review

Background: Throughout the Covid-19 pandemic artificial intelligence (AI) models were developed in response to significant resource constraints affecting healthcare systems. Previous systematic reviews demonstrate that healthcare datasets often have significant limitations, contributing to bias in any AI health technologies they are used to develop. This systematic review aimed to characterise the composition and reporting of datasets created throughout the Covid-19 pandemic, and highlight key deficiencies which could affect downstream AI models. Methods: A systematic search of MEDLINE identified articles describing datasets used for AI health technology development. Studies were screened for eligibility, and datasets collated for analysis. Google Dataset Search was used to identify additional datasets. After deduplication and exclusion of datasets not related to Covid-19 or those not containing data relating to individual humans, dataset documentation was assessed for the completeness of metadata reporting, their composition, the means of data access and any restrictions, ethical considerations, and other factors. Findings: 192 datasets were analysed. Metadata were often incomplete or absent. Only 48% of datasets’ documentation described the country where data originated, 43% reported the age of individuals included, and under 25% reported sex, gender, race, ethnicity or any other attributes. Most datasets provided no information on data labelling, ethical review, or consent for data sharing. Many datasets reproduced data from other datasets, sometimes without linking to the original source. We found multiple cases where paediatric chest X-ray images from prior to the Covid-19 pandemic were reproduced in datasets without this being acknowledged. Interpretation: This review highlights substantial deficiencies in the documentation of many Covid-19 datasets. It is imperative to balance data availability with data quality in future health emergencies, or else we risk developing biased AI health technologies which do more harm than good. Funding: This review was funded by The NHS AI Lab and The Health Foundation, and supported by the National Institute for Health and Care Research (AI_HI200014)

Revealing transparency gaps in publicly available Covid-19 datasets used for medical artificial intelligence development:a systematic review

Background: Throughout the Covid-19 pandemic artificial intelligence (AI) models were developed in response to significant resource constraints affecting healthcare systems. Previous systematic reviews demonstrate that healthcare datasets often have significant limitations, contributing to bias in any AI health technologies they are used to develop. This systematic review aimed to characterise the composition and reporting of datasets created throughout the Covid-19 pandemic, and highlight key deficiencies which could affect downstream AI models.Methods: A systematic search of MEDLINE identified articles describing datasets used for AI health technology development. Studies were screened for eligibility, and datasets collated for analysis. Google Dataset Search was used to identify additional datasets. After deduplication and exclusion of datasets not related to Covid-19 or those not containing data relating to individual humans, dataset documentation was assessed for the completeness of metadata reporting, their composition, the means of data access and any restrictions, ethical considerations, and other factors.Findings: 192 datasets were analysed. Metadata were often incomplete or absent. Only 48% of datasets’ documentation described the country where data originated, 43% reported the age of individuals included, and under 25% reported sex, gender, race, ethnicity or any other attributes. Most datasets provided no information on data labelling, ethical review, or consent for data sharing. Many datasets reproduced data from other datasets, sometimes without linking to the original source. We found multiple cases where paediatric chest X-ray images from prior to the Covid-19 pandemic were reproduced in datasets without this being acknowledged. Interpretation: This review highlights substantial deficiencies in the documentation of many Covid-19 datasets. It is imperative to balance data availability with data quality in future health emergencies, or else we risk developing biased AI health technologies which do more harm than good.Funding: This review was funded by The NHS AI Lab and The Health Foundation, and supported by the National Institute for Health and Care Research (AI_HI200014).<br/