The Experts below are selected from a list of 144 Experts worldwide ranked by ideXlab platform
Tim Whiteley - One of the best experts on this subject based on the ideXlab platform.
-
enterprise in higher Education an overview from the Department for Education and employment
Journal of Education and Training, 1995Co-Authors: Tim WhiteleyAbstract:Gives an overview of the Enterprise in Higher Education (EHE) initiative from a Department for Education and Employment perspective, outlining the long– and short‐term goals. Describes the EHE process itself, including allocation of funds and contract management. Emphasizes the importance of employer partnerships, student enterprise and self‐evaluation within higher Education.
-
Enterprise in Higher Education – an overview from the Department for Education and Employment
Journal of Education and Training, 1995Co-Authors: Tim WhiteleyAbstract:Gives an overview of the Enterprise in Higher Education (EHE) initiative from a Department for Education and Employment perspective, outlining the long– and short‐term goals. Describes the EHE process itself, including allocation of funds and contract management. Emphasizes the importance of employer partnerships, student enterprise and self‐evaluation within higher Education.
Jonathan Muir Downs - One of the best experts on this subject based on the ideXlab platform.
-
an approach to linking Education social care and electronic health records for children and young people in south london a linkage study of child and adolescent mental health service data
BMJ Open, 2019Co-Authors: Jonathan Muir Downs, Tamsin Ford, Robert Stewart, Sophie Epstein, Hitesh Shetty, Ryan Little, Amelia JewellAbstract:OBJECTIVES: Creation of linked mental health, social and Education records for research to support evidence-based practice for regional mental health services. SETTING: The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013. PARTICIPANTS: A clinical cohort of 35 509 children and young people (aged 4-17 years). DESIGN: Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on Education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources. OUTCOME MEASURES: Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80% attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases. RESULTS: Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95% CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22). CONCLUSIONS: The work described sets a precedent for Education data being used for medical benefit in England. Linkage between health and Education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.
Amelia Jewell - One of the best experts on this subject based on the ideXlab platform.
-
an approach to linking Education social care and electronic health records for children and young people in south london a linkage study of child and adolescent mental health service data
BMJ Open, 2019Co-Authors: Jonathan Muir Downs, Tamsin Ford, Robert Stewart, Sophie Epstein, Hitesh Shetty, Ryan Little, Amelia JewellAbstract:OBJECTIVES: Creation of linked mental health, social and Education records for research to support evidence-based practice for regional mental health services. SETTING: The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013. PARTICIPANTS: A clinical cohort of 35 509 children and young people (aged 4-17 years). DESIGN: Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on Education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources. OUTCOME MEASURES: Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80% attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases. RESULTS: Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95% CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22). CONCLUSIONS: The work described sets a precedent for Education data being used for medical benefit in England. Linkage between health and Education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.
Sophie Epstein - One of the best experts on this subject based on the ideXlab platform.
-
an approach to linking Education social care and electronic health records for children and young people in south london a linkage study of child and adolescent mental health service data
BMJ Open, 2019Co-Authors: Jonathan Muir Downs, Tamsin Ford, Robert Stewart, Sophie Epstein, Hitesh Shetty, Ryan Little, Amelia JewellAbstract:OBJECTIVES: Creation of linked mental health, social and Education records for research to support evidence-based practice for regional mental health services. SETTING: The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013. PARTICIPANTS: A clinical cohort of 35 509 children and young people (aged 4-17 years). DESIGN: Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on Education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources. OUTCOME MEASURES: Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80% attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases. RESULTS: Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95% CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22). CONCLUSIONS: The work described sets a precedent for Education data being used for medical benefit in England. Linkage between health and Education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.
Robert Stewart - One of the best experts on this subject based on the ideXlab platform.
-
an approach to linking Education social care and electronic health records for children and young people in south london a linkage study of child and adolescent mental health service data
BMJ Open, 2019Co-Authors: Jonathan Muir Downs, Tamsin Ford, Robert Stewart, Sophie Epstein, Hitesh Shetty, Ryan Little, Amelia JewellAbstract:OBJECTIVES: Creation of linked mental health, social and Education records for research to support evidence-based practice for regional mental health services. SETTING: The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013. PARTICIPANTS: A clinical cohort of 35 509 children and young people (aged 4-17 years). DESIGN: Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on Education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources. OUTCOME MEASURES: Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80% attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases. RESULTS: Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95% CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22). CONCLUSIONS: The work described sets a precedent for Education data being used for medical benefit in England. Linkage between health and Education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.