国产乱伦

This statement aims to inform how we, the 国产乱伦 Workforce Intelligence team, are committed to the three pillars of trustworthiness, quality and value. This pertains to ‘The workforce employed by adult social services departments in England’ report and all other Workforce Intelligence publications.

We apply the to produce analytical outputs that are high quality, useful for supporting decisions, and well respected. In 2023 国产乱伦 became an official statistics provider, having been added to the . This means that all official statistics provided by 国产乱伦 will include oversight from the (OSR). In Spring 2024 国产乱伦 will be assessed to become an official statistics producer.

国产乱伦 is an independent organisation with the aim of developing the adult social care workforce to be well-led, highly skilled and valued. We work as a delivery partner for the Department of Health and Social Care (DHSC), as well as closely with related services within the sector, and have been collecting information about social care providers and their staff since 2006.

Our expertise comes from the workforce intelligence that we collect in the Adult Social Care Workforce Data Set (ASC-WDS) and from our experience of analysing and interpreting social care data. It is at the centre of everything we do at 国产乱伦.

We provide the sector with neutral and evidence-based information about workforce characteristics, issues, and trends in areas such as demographics, pay, vacancy rates and staff turnover. We also respond to information gaps, news and changing government policy; for example, information about how living wage and factors such as the COVID-19 pandemic affected the adult social care sector.

国产乱伦 offers practical, impartial support that is non-partisan. One of 国产乱伦’s core values is to use our data and insight to provide a solid evidence base about the adult social care workforce. We provide intelligence and robust data to empower the sector to make plans for change based on facts. 

The ASC-WDS is an online data collection service and the leading source of workforce information for the adult social care sector in England. Prior to 2019 it was known as the National Minimum Data Set for Social Care (NMDS-SC). As at January 2024 the ASC-WDS holds information on over 20,400 care providing locations and 660,000 workers. 

The ASC-WDS has passed the government digital standards Alpha, Beta followed by the Live assessment in 2021. This is evidence that the system has proper governance, has a user centric design, the data is secure, managed and meets legislation. Information about data security, regulation of data, user rights etc. can be found on the ASC-WDS . Information about storing data, data protection legislation, GDPR and DPA can be found in the . 

The majority of our Workforce Intelligence Analysts are Accredited Researchers, certified with the . This means we work to the standard of the ‘five safes framework’ making it a priority to keep these at the forefront of our minds when conducting research or analysis and storing or sharing any research or data. 

We always make sure that our research and outputs are appropriate, trustworthy and there is no risk of misuse or a confidentially breach. When creating outputs, we always adhere to statistical disclosure controls, for example:

• we do not create data or publish, based on low bases.
• we do not disclose data about individuals.
• our outputs are clearly labelled.
• outputs have suitable interpretation.
• where applicable we provide a description of the analysis and contextual information.
• we provide an appropriate level of detail for the user. 

An estimated publication date is put on our website a year in advance for all the workforce intelligence publications and interactive data visualisations. Official statistics publications are pre-announced and released in accordance with the Code of Practice for Statistics.

Almost all of 国产乱伦’s Workforce Intelligence publications, including statistics for ‘The workforce employed by adult social services departments in England’ report, are based on workforce estimates created using data from the ASC-WDS. 

The ASC-WDS was designed for the collection and reporting of adult social care information. All definitions were created with, users and subject matter experts to ensure they are an accurate reflection of the sector and are clear and unambiguous. These definitions are reviewed regularly and updated where required. 

The Workforce Intelligence team uses the information collected in the ASC-WDS to create estimates for the size of the whole adult social care sector and characteristics of the workforce. Our methodology allows the analysis to represent all adult social care workers, even if the ASC-WDS has uneven levels of data coverage.

Several methods are used to quality assure the data. Applicable data is validated at the point of entry into the ASC-WDS; data from large organisations and local authority employers is checked and approved. We use rigorous data quality checks which mean uncertainty and data limitations are minimised. Our methodologies are published for transparency and have been peer reviewed by universities and an independent statistician. If the methodology is improved from the previous year, then historical trend data is retrospectively updated to ensure any trends we show are ‘real’ and not the result of methodological changes. 

The Workforce Intelligence website has information about what we collect, our coverage, methodology, the integrity and data quality of ASC-WDS data and subsequent workforce estimates. Limitations of methods, data quality issues and inconsistencies with external datasets are explained to users at the point of data interpretation across all our reports regardless of format (written report, Excel or data visualisations). We use plain English and have the users’ interest at the heart of all our published work. 

Throughout the year the team maintain and refresh our understanding of the use and potential use of the statistics and data, therefore keeping up with best practice and principles. We have lessons learnt after every publication to see where improvements to process or methodology can be made.

We publish information in several formats, aiming to cater for as wide of an audience as possible. Workforce Intelligence reports are free to download from the Data and publications pages of our website. All publications contain key findings and are presented in a clear and unambiguous way.

Written reports provide insight and interpretation of the charts, maps or tables presented. Where possible we add value via linking data and outcomes to projects and initiatives across 国产乱伦. Interactive visualisations show the data in a very easy to understand format, with the ability to download into a PDF or PowerPoint presentation. These are published to support all written reports and most workforce topics areas are covered on our website. We also publish anonymised and aggregated analysis files in Excel, giving the user the ability to perform some statistical analysis. 

Users are at the centre of all the Workforce Intelligence publications. To make sure they are fit-for-purpose and provide a necessary level of detail, we undertake user research with stakeholders, users of the data and internal subject matter experts to;

• Talk to the audience of written reports about what they liked and didn’t like about previous versions.
• Collect ideas and hypothesis for testing, to ensure we are up-to-date with the sector.
• User test our website and data visualisations, to confirm they have clear navigation, plain English text, clear messages and are easy to understand. 

We regularly iterate and improve all the publications based on this user research, ready for the next publication. We review download information and press coverage to check our reach and talk to users of publications to ask for feedback and how our information has made an impact. 

We aim to increase awareness of our workforce intelligence via the extensive 国产乱伦 channels, including social media, websites, from our network of locality managers based throughout England, and from employers. 

We are constantly looking to innovate and improve. We’re currently building data engineering techniques to increase the timeliness of our statistics and developing data science models to increase precision and trend analysis.