By looking at existing data on actual staff in different settings (like GP practices) we can identify which areas are struggling the most with issues like staff shortages and high turnover, and statistical analysis can tell us if any features of those settings area associated with higher or lower risk of challenges. These insights are essential for making sure that the rest of the project focuses its efforts where they are most needed. Without this evidence, it would be difficult to know where to start or which problems to prioritise in developing the intervention.
As well as identifying priorities for the team, secondary data analysis supports the development of a broader understanding of what makes a workforce more or less sustainable.
- For more information on what “sustainable” means in healthcare education, please see this article produced by members of the Workforce Voices team
By identifying patterns and risk factors, for example whether rural areas or more deprived communities face greater challenges, we can ensure that the project’s interventions are targeted, relevant, and more likely to succeed. It also feeds into the theoretical work of the project, helping to explain how and why workforce problems develop.
Approach and methods
The team will use a range of existing data sources, including national workforce datasets and local information from NHS organisations.
We will combine this with geographical and demographic data, such as how rural an area is or how deprived the local population might be. Using statistical techniques, we will identify if there are patterns in workforce problems (such as high turnover, or sickness rates, or reliance on temporary staff) which might indicate locations of higher or lower risk. We will also look at what factors are linked to these risks.
This workstream will produce valuable insights that can be used by NHS leaders, local authorities, and policymakers to plan better support for staff and allocate resources. It will help identify where interventions are most urgently needed and what kind of support might be most effective. The findings will also contribute to national discussions about workforce planning and health inequalities. By showing how local conditions affect workforce stability, the workstream can help shape more targeted, fair, and sustainable solutions.
Challenges and Limitations
While secondary data analysis presents many advantages, it also comes with its own set of challenges and limitations. It’s crucial to critically evaluate the sources so that any biases or inaccuracies can be identified and addressed.
- Quality and reliability of the data: since the data comes from various existing sources, it may differ in definition, scope, and quality, which could affect the analyses conducted.
- Temporal relevance of data: workforce issues can fluctuate significantly over time, and older datasets may not accurately reflect current conditions or emerging trends. Although the analysis aims to identify patterns and solutions, the evolving nature of healthcare services means that findings could become outdated quickly, necessitating ongoing research and updates.
- Possibility of gaps in data: particularly in relation to smaller or rural areas, where specific workforce challenges may be overlooked.
Ensuring comprehensive coverage across various demographics and regions is vital for the effectiveness of the workstream.
Opportunities in this workstream
Some of the data we are considering, and the challenges we identify, will need experts in those areas of work to help us interpret them, and produce meaningful findings. This will be done through our ongoing Community Inclusion and Engagement work, but we will also welcome any feedback on our use of data, and direction to relevant data sources.
