Developing a nationwide registry of UK veterans seeking help from sector charities—a machine learning approach to stratification
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| Publicado en: | European Journal of Public Health vol. 35, no. 1 (Feb 2025), p. 5 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Oxford University Press
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 001 | 3253586023 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1101-1262 | ||
| 022 | |a 1464-360X | ||
| 024 | 7 | |a 10.1093/eurpub/ckae141 |2 doi | |
| 035 | |a 3253586023 | ||
| 045 | 2 | |b d20250201 |b d20250228 | |
| 084 | |a 53202 |2 nlm | ||
| 100 | 1 | |a Serra, Giuseppe |u Department of Nursery, Midwifery and Health, Northumbria University , Newcastle upon Tyne, United Kingdom | |
| 245 | 1 | |a Developing a nationwide registry of UK veterans seeking help from sector charities—a machine learning approach to stratification | |
| 260 | |b Oxford University Press |c Feb 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The assistance to veterans in the UK is provided by the National Health Service and over 1800 military charities. These charities count services using different definitions and reporting systems, so to date a national registry of service usage does not exist. The aim of the Map Of Need Aggregation ResearCH study is to build a standardized registry of service usage data for the military charity sector. Data are completely anonymized by adopting a Secure Hashing Algorithm. A unique anonymous identifier is generated allowing both privacy protection and avoiding double counts. Data are standardized and linked with an automated process to create an aggregated dataset. The dataset describes the population, using both a priori and machine learning approaches. To date a total of 42 509 veterans with 128 423 needs are included. The mean age was 60.1 years, and 90% were male. 65% were receiving other benefits, 5% were homeless and 1% were in prison. 65% of the needs recorded concerned social wellbeing. 40% of veterans received assistance in at least two different years. The k-means clustering approach returned 4 subgroups of use that were identical to those created using a priori knowledge. The dataset is the most comprehensive source of veteran charity usage data in the UK to date. Service usage is generally homogenous among subgroups, but some differences were highlighted indicating that younger, non-officer veterans may be more at risk of presenting with more complex needs. These first useful insights can help allocate resources to build an effective preventive strategy for more complex cases. | |
| 651 | 4 | |a United Kingdom--UK | |
| 653 | |a Homelessness | ||
| 653 | |a Datasets | ||
| 653 | |a Help seeking behavior | ||
| 653 | |a Charities | ||
| 653 | |a Needs | ||
| 653 | |a Health services | ||
| 653 | |a Hash based algorithms | ||
| 653 | |a Veterans | ||
| 653 | |a Homeless people | ||
| 653 | |a Machine learning | ||
| 653 | |a Well being | ||
| 653 | |a Social interest | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Use statistics | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Data | ||
| 653 | |a Subgroups | ||
| 653 | |a Clustering | ||
| 653 | |a Privacy | ||
| 653 | |a Vector quantization | ||
| 653 | |a Military service | ||
| 653 | |a Social well being | ||
| 653 | |a Stratification | ||
| 653 | |a Resource allocation | ||
| 653 | |a Prevention | ||
| 653 | |a Social | ||
| 700 | 1 | |a Tomietto, Marco |u Department of Nursery, Midwifery and Health, Northumbria University , Newcastle upon Tyne, United Kingdom | |
| 700 | 1 | |a McGill, Andrew |u Department of Nursery, Midwifery and Health, Northumbria University , Newcastle upon Tyne, United Kingdom | |
| 700 | 1 | |a Kiernan, Matthew |u Department of Nursery, Midwifery and Health, Northumbria University , Newcastle upon Tyne, United Kingdom | |
| 773 | 0 | |t European Journal of Public Health |g vol. 35, no. 1 (Feb 2025), p. 5 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3253586023/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3253586023/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |