The comparative effectiveness and efficiency of Cognitive Behaviour Therapy (CBT), Counselling for Depression (CfD) and other High Intensity Therapies (HIT) in the treatment of depression in the Improving Access to Psychological Therapies (IAPT) service.

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Objective for processing

[4 paragraphs unchanged] The University of Sheffield is was funded by The British Association for Counselling and Psychotherapy (BACP) in the [105 words unchanged] CBT, CfD as well as potentially the IAPT therapies designated as ‘other’. [1 paragraph unchanged] Most of the analysis of the IAPT data to date has not acknowledged the difference in related groupings (e.g., outcomes for patients of a particular CCG are likely to be related, and different in some way from the outcomes of another CCG). Thus, a more rigorous statistical analysis (multilevel modelling: MLM) ought to be conducted that accounts for the naturally nested nature of the data. The University of Sheffield wish to retain data from the Improving Access to Psychological Therapies Dataset to carry out this analysis. The dataset is limited to those patients who had received a high intensity treatment during their episode of care. Patient demographic variables and variables describing the care pathway were required to carry out the analysis. The larger the data sample, the more likely that accurate, reliable results will be produced, particularly when applying MLM. The NHS Digital, National IAPT dataset is unique in being able to address the research questions. No additional funding is available from the funders (BACP) so the work for publication is now being carried out using internal funding from the Psychology Department in the University of Sheffield, in order to complete the agreed tasks. In order to carry out MLM to assess the relative effectiveness of high intensity treatments, patient characteristics need to be controlled for, therefore data at the patient level (demographic variables treatment received and outcome scores) are required. Such analysis would also identify patient characteristics associated with better or poorer outcomes. Having national data across 3 years provides a larger dataset and also allows for the testing of any changes over time and the effects of different CCGs. A larger data sample would also allow the testing of possible complex interactions between predictor variables and subgroup analyses for specific demographic groups (e.g. older patients, younger patients, males, females etc). Most of the analysis of the IAPT data to date has not acknowledged the difference in related groupings (e.g., outcomes for patients of a particular Integrated Care Board (ICB) are likely to be related, and different in some way from the outcomes of another ICB). Thus, a more rigorous statistical analysis (multilevel modelling: MLM) ought to be conducted that accounts for the naturally nested nature of the data. The University of Sheffield wish to retain data from the Improving Access to Psychological Therapies Dataset to carry out this analysis. The dataset is limited to those patients who had received a high intensity treatment during their episode of care. Patient demographic variables and variables describing the care pathway were required to carry out the analysis. The larger the data sample, the more likely that accurate, reliable results will be produced, particularly when applying MLM. The NHS England, National IAPT dataset is unique in being able to address the research questions. In order to carry out MLM to assess the relative effectiveness of high intensity treatments, patient characteristics need to be controlled for, therefore data at the patient level (demographic variables treatment received and outcome scores) are required. Such analysis would also identify patient characteristics associated with better or poorer outcomes. Having national data across 3 years provides a larger dataset and also allows for the testing of any changes over time and the effects of different ICBs. A larger data sample would also allow the testing of possible complex interactions between predictor variables and subgroup analyses for specific demographic groups (e.g. older patients, younger patients, males, females etc). [3 paragraphs unchanged] The GDPR legal basis for processing is covered by article 6(1)(e) (processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller) and article 9(2)(j) (processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection, and provide suitable and specific measures to safeguard the fundamental rights and the interests of the data subject. The lawful basis for processing personal data under the UK GDPR is: Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller The lawful basis for processing special category data under the UK GDPR is: Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject. [2 paragraphs unchanged]

Processing activities

There was a flow of data out of NHS Digital England in the form of pseudonymised Improving Access to Psychological Therapies (IAPT) data [16 words unchanged] data only. There are no further flows of data under this agreement. All pseudonymised record level data from NHS Digital England is stored in a dedicated space on the University of Sheffield server. [52 words unchanged] of Sheffield laptop. This is expected to continue for the foreseeable future. . The laptop is password protected and encrypted. Access to the ‘virtual machine’ [38 words unchanged] (Cyber Safety, Cyber Essentials Assured Computing, Protecting Information and Protecting Personal Data). [9 paragraphs unchanged]

Expected output

[2 paragraphs unchanged] Findings of this study were described in a paper submitted to Psychological [20 words unchanged] was considered to be generalised. The team is in the process of rewriting finalising a rewrite of the paper and adjusting the title. The paper will be submitted to Journal of Affective Disorders. A second paper is being prepared to submit by July 2022 December 2023 with the intention of submitting the paper to BMC Psychiatry, British Journal of Clinical Psychology or Journal of Applied Psychology. [13 paragraphs unchanged] Since March 2021 the data has been prepared and analysed and results of one study will be rewritten and resubmitted for publication in Psychological Medicine. A presentation of these results has been accepted at Society for Psychotherapy Research Conference in Denver (July 2022). Since March 2021 the data has been prepared and analysed and a paper was submitted to the Journal of affective Disorders in August 2023.

Expected measurable benefits

[3 paragraphs unchanged] Prior to March 2020, work on the data set involved managing some [65 words unchanged] other datasets but are informed by the team's experience preparing the NHS Digital England data. The three students had no access to the NHS Digital England Data. Due to the COVID-19 pandemic and associated closure of university premises, it [11 words unchanged] 2020 and March 2021. Therefore, the final preparations and the main analysis have been were delayed. The rejection of the paper by Psychological Medicine resulted in some re-analysis and a major re-write which has caused further delay, which has had a knock-on effect on the planned second analysis and paper.

Benefits reported

Due to the impact of COVID-19 university closure; and, additional time taken to install IT security systems, there are no yielded benefits to date. A presentation of early findings was made at Society for Psychotherapy Research (SPR) Conference in Denver (July 2022). More yielded benefits are expected in line with the outputs described.

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Objective for processing

This extension agreement is to allow the University of Sheffield to hold and process pseudonymised Improving Access to Psychological Therapies (IAPT) data that flowed under a previous version of this agreement. There are no further flows of data under this agreement. [1 paragraph unchanged] Previously, the University has successfully requested and been granted access to IAPT [8 words unchanged] of the National Audit of Psychological Therapies (NAPT). The University of Sheffield now wish also have access to access the IAPT CBT, CfD and other HIT’s data in order to build on work examining outcomes for these therapies. [8 paragraphs unchanged] The GDPR legal basis for processing is covered by article 6(1)(e) (processing [60 words unchanged] to the aim pursued, respect the essence of the right to data protection protection, and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject). subject. For data minimisation purposes, only IAPT data for 2014/15, 2015/16, and 2016/17 years were requested and only data fields that have been deemed necessary for the purpose of this research have been disseminated. There are no moral, ethical issues, or risk of potential harm to the public raised by the continued processing of the data, and there are no alternative, less intrusive ways of achieving the purpose of this application.

Processing activities

All pseudonymised record level data from NHS Digital is stored in a dedicated space on the University of Sheffield server. It is only accessible from a single, identifiable PC and datapoint within the Department of Psychology. Only a single data analyst, substantively employed by the University of Sheffield, can access the data. Due to COVID restrictions, the PC datapoint is currently accessed remotely by a University of Sheffield laptop. The laptop is password protected and encrypted. Access to the ‘virtual machine’ and the datapoint giving access to the server are each password protected including a telephone confirmation before accessing. This set-up has been incorporated in the recent training which the analyst has undertaken and is required to undertake annually (Cyber Safety, Cyber Essentials Assured Computing, Protecting Information and Protecting Personal Data). There was a flow of data out of NHS Digital in the form of pseudonymised Improving Access to Psychological Therapies (IAPT) data under a previous version of this agreement. This extension agreement is to hold and process the data only. There are no further flows of data under this agreement. Aggregate-level data, for example SPSS output (with small numbers supressed), will flow from the dedicated server space to a shared file (also on the University of Sheffield server) to which only the analyst and the PI will have access. Aggregated data and results of analysis will flow between the University of Sheffield and BACP in order to keep the full team informed and prepare publications., The pseudonymised record-level data will remain on the University of Sheffield server at all times. BACP's involvement will only be to support and collaborate on outputs from the research and nobody at BACP will have any access to the record-level data. There will be no requirement or attempt to re-identify individuals from the data and no linkages will be made with other data. All pseudonymised record level data from NHS Digital is stored in a dedicated space on the University of Sheffield server. It is only accessible from a single, identifiable PC and datapoint within the Department of Psychology. Only a single data analyst, substantively employed by the University of Sheffield, can access the data. Due to the Data Analyst being vulnerable to Covid 19, the PC datapoint is being accessed remotely via a University of Sheffield laptop. This is expected to continue for the foreseeable future. . The laptop is password protected and encrypted. Access to the ‘virtual machine’ and the datapoint giving access to the server are each password protected including a telephone confirmation before accessing. This set-up has been incorporated in the recent training which the analyst has undertaken and is required to undertake annually (Cyber Safety, Cyber Essentials Assured Computing, Protecting Information and Protecting Personal Data). Aggregate-level data, for example, SPSS output (with small numbers suppressed), will flow from the dedicated server space to a shared file (also on the University of Sheffield server) to which only the analyst and the PI will have access. Aggregated data and results of analysis will flow between the University of Sheffield and BACP in order to keep the full team informed and prepare publications., The pseudonymised record-level data will remain on the University of Sheffield server at all times. BACP's involvement will only be to support and collaborate on outputs from the research and nobody at BACP will have any access to the record-level data. There will be no requirement or attempt to re-identify individuals from the data and no linkages will be made with other data. [2 paragraphs unchanged] Disclosure control policy: In order to protect patient confidentiality, when presenting results calculated from IAPT record level data, the following suppression rules are to be applied: · any figures based on a count of between 0 and 4 referrals are to be suppressed by replacing the number with an asterisk (*); · all sub-national counts are to be rounded to the nearest 5; · sub-national rates (which are presented as percentages and are based on unrounded numbers) are to be rounded to the nearest whole percent. · national rates are to be rounded to one decimal place.

Expected output

All outputs stemming from this work would will be presented at an aggregated level small numbers suppressed only, no record-level data will be disseminated. The team aim to publish a minimum of one key paper of [71 words unchanged] analysis, and therefore any publications resulting from this analysis, have been delayed. The aim now is to submit this paper by 31st December 2021. Journals may include: BMC Psychiatry, British Journal of Clinical Psychology, Journal of Applied Psychology Findings of this study were described in a paper submitted to Psychological Medicine but this was rejected as the publication is a United States centred journal and findings from the IAPT data was considered to be generalised. The team is in the process of rewriting the paper and adjusting the title. Following publication in a peer-reviewed academic journal researchers plan to disseminate the findings to different audiences as detailed below. A second paper is being prepared to submit by July 2022 with the intention of submitting the paper to BMC Psychiatry, British Journal of Clinical Psychology or Journal of Applied Psychology. Following publication in a peer-reviewed academic journal, researchers plan to disseminate the findings to different audiences as detailed below. [1 paragraph unchanged] An article would will be written for the BACP practitioner journal ‘Therapy Today’ which is circulated to all 47,000+ BACP members. A brief summary of the findings would will also be included in the BACP e-bulletin which is disseminated to all BACP members. Alongside the written dissemination of the findings findings, the plan is to submit to present the findings at a number of academic conferences, including the BACP Annual Research Conference, the Society for Psychotherapy Research UK and International conferences UK, international conferences, and the British Psychological Society conference. Additionally, there may be other events that this work would be relevant to present at to reach a wide range of audiences. [9 paragraphs unchanged] Since March 2021 the data has been prepared and analysed and results of one study will be rewritten and resubmitted for publication in Psychological Medicine. A presentation of these results has been accepted at Society for Psychotherapy Research Conference in Denver (July 2022).

Expected measurable benefits

The planned analyses of the requested data will be of benefit to the health and social care system [75 words unchanged] psychological therapies compare to one another in terms of effectiveness for patients. Through informing commissioners of the findings of this work, for example via [40 words unchanged] base of counselling and psychotherapy practitioners by disseminating to the 57,000 BACP membership. members. [1 paragraph unchanged] Prior to March 2020, work on the data set involved managing some technical issues associated with the size of the dataset, as well as understanding the data structure. This took longer than was originally anticipated, but the gained knowledge of the data structure of IAPT service data and the process of preparing it for analysis has informed planned studies of other IAPT datasets within the University. Three students in Data Science have completed their Masters degrees using other datasets but are informed by the team's experience preparing the NHS Digital data. The three students had no access to the NHS Digital Data. Due to the COVID-19 pandemic and associated closure of university premises, it was not possible to access the current data files between March 2020 and March 2021. Therefore, the final preparations and the main analysis have been delayed.

Benefits reported

Prior to March 2020, the work on the data set involved managing some technical issues associated with the size of the dataset, as well as understanding the data structure. This took longer than was originally anticipated, but the gained knowledge of the data structure of IAPT service data and the process of preparing it for analysis has informed planned studies of other IAPT datasets within the University. So far, three Masters degrees in Data Science have been taken up where the knowledge gained in preparing the current data will be applied to other datasets. Due to the impact of COVID-19 university closure; and, additional time taken to install IT security systems, there are no yielded benefits to date. Due to the COVID-19 pandemic and associated closure of university premises, it was not possible to access the current datafiles between March 2020 and March 2021. Therefore, the final preparations and the main analysis have been delayed. Nonetheless, the aspirations for benefits from analysis of the dataset remain as previously stated.

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Objective for processing

The University of Sheffield (the School for Health and Related Research (ScHARR)), funded by the British Association of Counselling and Psychotherapy (BACP), (ScHARR)) aim to examine the effectiveness of High Intensity Therapies (HIT) in the [59 words unchanged] a broad equivalence in outcomes between these three therapy types in IAPT. However, the analysis of the IAPT data to date has not acknowledged the difference in related groupings (e.g. outcomes for patients of therapist A are likely to be related, as are outcomes of a particular service, as are outcomes of a particular CCG). Thus, a more rigorous statistical analysis ought to be conducted that accounts for the naturally nested nature of the data. Previously, the University has successfully requested and been granted access to IAPT data collected as part of the 2nd round of the National Audit of Psychological Therapies (NAPT). The University of Sheffield now wish to access the IAPT CBT, CfD and other HIT’s data in order to build on work examining outcomes for these therapies. Using multi-level modelling techniques that address the nested nature of the IAPT data, would allow researchers to evaluate both the overall effectiveness of the three HIT as well as to evaluate how other variables (including service-level variables) impact therapy outcome. It is intended that the research will increase understanding of the variables that significantly impact the effectiveness of IAPT HIT interventions, which has the potential to lead to improvements in HIT outcomes for IAPT patients. It is important to undertake a rigorous analysis of this data in order to ensure that the psychological therapies available within IAPT are providing comparable outcomes for patients. Evaluating the contribution of service-level variables may suggest potential pathways for service improvement, for example if the impact of the service-level variables on patient outcomes is significant and/or very variable across services. Equally it is important to evaluate the importance of patient-level variables, for example to explore whether there is a differential effect of patient intake-severity or socio-economic status on outcomes in the three HITs. Again, a more nuanced understanding of the inter-relations between patient and therapy (intervention) variables can suggest pathways to service improvement and improved patient outcomes, for example by finding that some interventions are better suited to patients with more moderate levels of difficulties. The NAPT data was collected between 2012 and 2013, prior to the roll-out of CfD training in 2011, and therefore the counselling data included was that of generic counselling rather than CfD. The analysis of the available data demonstrated CBT and counselling to be equally effective in the treatment of depression and multi-level modelling applied to the data indicated site variability that was not affected by therapy type. The University of Sheffield researchers wish to build on this work by conducting a similar analysis of more recent IAPT data, focusing on CfD as the NICE approved form of counselling available in IAPT. This work is also important as there is currently no research outside of the most recent IAPT report supporting the effectiveness of CfD and yet it has been rolled out across IAPT. There is clear evidence from randomised controlled trials as to the effectiveness of CBT, however, there is currently no such evidence for CfD. CfD was developed following the 2009 publication of the NICE guidelines for Depression in Adults in which there was some evidence in support of counselling, specifically from person-centred and emotion-focused approaches. CfD aimed to provide an evidence-based manualised form of counselling that could be delivered within IAPT. The University of Sheffield is funded by The British Association for Counselling and Psychotherapy (BACP) in the production of outputs from the research. For researchers in the Mental Health Research Unit at ScHARR, analysis of the data will be a continuation of a programme of work using Multilevel Modelling (MLM) with large routinely collected data samples from different sources. In addition to answering the research questions, researchers are interested in the application and development of MLM methodology. The BACP has a commitment to championing the need of patients, as well as the counselling professions broadly. As the largest professional body for the counselling professions in the UK, their 57,000 members work across a wide range of therapeutic modalities, including within IAPT providing CBT, CfD as well as potentially the IAPT therapies designated as ‘other’. Similarly, it is important to evaluate the effectiveness of the HIT labelled ‘other’, as while it is unclear exactly what these interventions consist of, they appear to be as effective as CBT and they are the second most available therapy provided within IAPT. The IAPT recommended therapy types have been designated as such based on empirically-informed recommendations thus it is in the interests of the public to evaluate also this ‘other’ therapy type if it is doing as well as the IAPT recommended therapies. Additionally, this work will complement a large scale randomised controlled trial which was recently completed (not using IAPT data provided in this agreement) that compared the efficacy and cost-effectiveness of CfD with CBT as delivered within an IAPT service. The University of Sheffield wishes to access the IAPT CBT, CfD and other HIT’s data in order to build on work examining outcomes for these therapies. Prior to this request the University has successfully requested and been granted access to IAPT data collected as part of the 2nd round of the National Audit of Psychological Therapies (NAPT). (Please note: this work was conducted on audit data only, accessed through HQIP). Most of the analysis of the IAPT data to date has not acknowledged the difference in related groupings (e.g., outcomes for patients of a particular CCG are likely to be related, and different in some way from the outcomes of another CCG). Thus, a more rigorous statistical analysis (multilevel modelling: MLM) ought to be conducted that accounts for the naturally nested nature of the data. The University of Sheffield wish to retain data from the Improving Access to Psychological Therapies Dataset to carry out this analysis. The dataset is limited to those patients who had received a high intensity treatment during their episode of care. Patient demographic variables and variables describing the care pathway were required to carry out the analysis. The larger the data sample, the more likely that accurate, reliable results will be produced, particularly when applying MLM. The NHS Digital, National IAPT dataset is unique in being able to address the research questions. The NAPT data was collected between 2012 and 2013, prior to the roll-out of CfD training in 2011 and therefore the counselling data included was that of generic counselling rather than CfD. The analysis of the available data demonstrated CBT and counselling to be equally effective in the treatment of depression and multi-level modelling applied to the data indicated site variability that was not effected by therapy type. In order to carry out MLM to assess the relative effectiveness of high intensity treatments, patient characteristics need to be controlled for, therefore data at the patient level (demographic variables treatment received and outcome scores) are required. Such analysis would also identify patient characteristics associated with better or poorer outcomes. Having national data across 3 years provides a larger dataset and also allows for the testing of any changes over time and the effects of different CCGs. A larger data sample would also allow the testing of possible complex interactions between predictor variables and subgroup analyses for specific demographic groups (e.g. older patients, younger patients, males, females etc). ScHARR wish to build on this work by conducting a similar analysis of more recent IAPT data, focusing on CfD as the NICE approved form of counselling available in IAPT. It is intended that the research will increase understanding of the variables that significantly impact the effectiveness of IAPT HIT interventions, which has the potential to lead to improvements in HIT outcomes for IAPT patients. It is important to undertake a rigorous analysis of this data in order to ensure that the psychological therapies available within IAPT are providing comparable outcomes for patients. The University of Sheffield is funded by The British Association for Counselling and Psychotherapy (BACP) in the production of outputs from the research. For researchers in the Mental Health Research Unit at ScHARR, analysis of the data will be a continuation of a programme of work using Multilevel Modelling (MLM) with large routinely collected data samples from different sources. In addition to answering the research questions, researchers are interested in the application and development of MLM methodology. The BACP has a commitment to championing the need of patients as well as the counselling professions broadly. As the largest professional body for the counselling professions in the UK their 45,000 members work across a wide range of therapeutic modalities, including within IAPT providing CBT, CfD as well as potentially the IAPT therapies designated as ‘other’. Additionally, this work will complement a large scale randomised controlled trial (not using IAPT data provided in this agreement) currently underway which is comparing the efficacy and cost-effectiveness of Counselling for Depression with CBT as delivered within an IAPT service. No data will be linked to the record-level patient data provided under this agreement, nor will it be passed onto other organisations. There will be no requirement or attempt to re-identify individuals from the data. [1 paragraph unchanged] The University of Sheffield is the sole Data Controller and Processor. No data will be linked to the record-level patient data provided under this agreement, nor will it be passed onto other organisations. There will be no requirement or attempt to re-identify individuals from the data. The GDPR legal basis for processing is covered by article 6(1)(e) (processing is necessary for the performance of a task in the public interest or in the exercise of official authority vested in the controller) and article 9(2)(j) (processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject).

Processing activities

All record-level data will be stored and analysed at the School for Health and Related Research (ScHARR) at the University of Sheffield. All pseudonymised record level data from NHS Digital is stored in a dedicated space on the University of Sheffield server. It is only accessible from a single, identifiable PC and datapoint within the Department of Psychology. Only a single data analyst, substantively employed by the University of Sheffield, can access the data. Due to COVID restrictions, the PC datapoint is currently accessed remotely by a University of Sheffield laptop. The laptop is password protected and encrypted. Access to the ‘virtual machine’ and the datapoint giving access to the server are each password protected including a telephone confirmation before accessing. This set-up has been incorporated in the recent training which the analyst has undertaken and is required to undertake annually (Cyber Safety, Cyber Essentials Assured Computing, Protecting Information and Protecting Personal Data). No data will be linked to record level patient data, nor will it be passed onto other organisations. There will be no requirement or attempt to re-identify individuals from the data. Aggregate-level data, for example SPSS output (with small numbers supressed), will flow from the dedicated server space to a shared file (also on the University of Sheffield server) to which only the analyst and the PI will have access. Aggregated data and results of analysis will flow between the University of Sheffield and BACP in order to keep the full team informed and prepare publications., The pseudonymised record-level data will remain on the University of Sheffield server at all times. BACP's involvement will only be to support and collaborate on outputs from the research and nobody at BACP will have any access to the record-level data. There will be no requirement or attempt to re-identify individuals from the data and no linkages will be made with other data. Record-level data will be analysed at the University of Sheffield. The ScHARR team has expertise in multi-level modelling and has previously analysed data collected from individual IAPT services and the NAPT dataset in this manner. Record-level data will be analysed at the University of Sheffield. Statistics software (SPSS and MLwiN) has been installed on the server in order to carry out the analysis within the protected server area. Analysis will include descriptive analysis of the sample as a whole and by treatment type, outcome comparisons for PHQ-9, and regression and multilevel modelling to identify predictors of outcome. Aggregate-level data (with small numbers supressed) will flow between the University of Sheffield and BACP in order to keep the full team informed as analysis progresses, however the pseudonymised record-level data will remain at the University of Sheffield. BACP's involvement will only be to support and collaborate on outputs from the research and nobody at BACP will have any access to the record-level data. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data). All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

Expected output

[1 paragraph unchanged] ScHARR The team aim to publish a minimum of one key paper of the findings [40 words unchanged] consider in order of priority, should the paper not be accepted initially. Due to an inability to access the data during the closure of the University due to COVID, analysis, and therefore any publications resulting from this analysis, have been delayed. The aim now is to submit this paper within 12 months of receiving the data for analysis. by 31st December 2021. [2 paragraphs unchanged] A full report would be disseminated internally within BACP the British Association for Counselling and Psychotherapy (BACP) and presented to the BACP Board of Governors. Governors and research committee. This would consist of aggregate data only. An article would be written for the BACP practitioner journal ‘Therapy Today’ which is circulated to all 45,000 47,000+ BACP members. A brief summary of the findings would also be included in the BACP E-bulletin e-bulletin which is disseminated to all BACP members. [2 paragraphs unchanged] • Political briefings (eg (e.g., parliamentarians, civil servants and Government departments) [3 paragraphs unchanged] • Professional bodies and mental health charities (eg (e.g., UKCP, BPS, Mind, Relate) • Via social media platforms (eg (e.g., Twitter) [2 paragraphs unchanged]

Expected measurable benefits

[1 paragraph unchanged] Through informing commissioners of the findings of this work, for example via [38 words unchanged] the knowledge base of counselling and psychotherapy practitioners by disseminating to the 45,000 57,000 BACP membership. [1 paragraph unchanged]

Benefits reported

Not stated in the previous version; added here.

Prior to March 2020, the work on the data set involved managing some technical issues associated with the size of the dataset, as well as understanding the data structure. This took longer than was originally anticipated, but the gained knowledge of the data structure of IAPT service data and the process of preparing it for analysis has informed planned studies of other IAPT datasets within the University. So far, three Masters degrees in Data Science have been taken up where the knowledge gained in preparing the current data will be applied to other datasets.

Due to the COVID-19 pandemic and associated closure of university premises, it was not possible to access the current datafiles between March 2020 and March 2021. Therefore, the final preparations and the main analysis have been delayed.

Nonetheless, the aspirations for benefits from analysis of the dataset remain as previously stated.

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Benefits reported

Stated in the previous version and removed here.

Yielded Benefits is not a requirement for new applications.

Unchanged: Objective for processing, Processing activities, Expected output, Expected measurable benefits.