Findings from 10,530 responses to the Edurio Staff Wellbeing and Working Conditions Survey
Over 10,000 academy staff members from across England have responded to our Staff Wellbeing and Working Conditions Survey so far.
Today, we published a report highlighting our first findings.
In this post, we’ll share a summary of the report’s key findings. If you would like to read the full report, you can download a copy here.
Amidst the growing staff retention problem in schools across England, there is an urgency to better understand the motivations behind those resigning their posts and to implement solutions that work on a large scale.
Multi-academy trusts across England were invited to participate in the Edurio Staff Wellbeing and Working Conditions Survey that was designed to provide actionable insights to the participating schools and trusts, but also to better understand what schools across England can do to improve staff retention.
As of April 2019 the survey has been completed by 10,530 respondents from 322 schools. It has become England’s largest independent study of the driving forces behind school staff staying in or leaving their posts.
Existing research, along with school leader interviews, highlighted six factors within a school’s control that have a material impact on staff retention. These factors make up Edurio’s Framework for Staff Retention, which guided survey design and analysis.
The analysis found that all six factors can have an impact on staff retention. Most importantly, it showed that many possible improvements are within the control of each individual school.
The report identifies common trends and key takeaways that may provide insight to school leaders and policymakers alike:
40% of academy staff are at risk of resigning their current post
Teachers and middle leaders are most at risk of leaving their position. 46% of teachers and 45% of middle leaders have considered resigning in the past three months.
← The extent of staff at risk of resigning is a school-by-school issue
Across the 322 participating schools, the percentage of staff at risk of resigning ranged from 0% in some schools to a staggering 84% in others. Although there were some differences between different types of schools and respondent groups, those were relatively minor and insufficient to explain the wide variation. This suggests that improving retention is within the control of each individual school.
Working Conditions and Relationships are both highly important for improving staff retention
Both survey themes (Working Conditions and Relationships) show a strong correlation with staff risk of resigning. This highlights the danger with focusing all efforts to improve staff retention on one singular issue.
Leadership Dynamics in the school is the strongest indicator of staff retention
Among the six factors explored in the survey, Leadership Dynamics showed the strongest correlation with staff risk of resigning. Leadership Dynamics measures whether relationships with school leaders are based on fairness, respect and staff engagement.
Many schools can improve Leadership Dynamics by increasing staff engagement in decision-making and welcoming staff feedback
Within Leadership Dynamics the lowest results were typically in questions asking staff members whether they felt their professional needs were understood by the leadership, whether they were consulted in decision-making, and whether their feedback to leadership had an impact.
Heavy workload is a widespread concern among the majority of teachers
When asked how often they feel overworked, two thirds of teacher respondents answered “Constantly” or “Often”, while only 4% said “Rarely” or “Never”. Further analysis points to data input, administrative tasks, and marking and assessment as potential starting points for reducing teacher workload.
We will be providing further deep dives and updates to this research, which you can follow here on the blog or by signing up for Edurio Insights. There are more multi-academy trusts joining the survey every month, which gives us the opportunity to repeat the analysis with an ever-expanding dataset and identify further insights.
This project has received funding from the European Union’s Horizon 2020SME programme for open and disruptive innovation under grant agreement №733984.