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Student Mental Health in UK Higher Education Institutions: Protocol for a Scoping Review of Trends, Gaps, and Research Directions

Student Mental Health in UK Higher Education Institutions: Protocol for a Scoping Review of Trends, Gaps, and Research Directions

For each included study, the following data will be extracted: Citation details: author(s), year of publication, journal, or source Study characteristics: study design, methodology, sample size, setting, and UK nation(s) covered Participant demographics: age, gender, ethnicity, student status (eg, undergraduate, postgraduate, international, first-generation, mature students) Mental health focus: key concepts and definitions used; types of mental health issue or condition explored (eg, anxiety, depression, psychological

Nicola Cogan, Aja Murray, Emily Long, Maria Gardani, Michelle O'Toole, Menchie Leung, Stella Kyratzi, Jelena Milicev, Katie Dryburgh, Chloe Bolota, Lacey Ho

JMIR Res Protoc 2025;14:e65594

Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis

Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis

Percentage of fall notes correctly classified by natural language processing approach versus International Classification of Diseases codes. a Class S: same-level. b Class M: multi-level. c Class U: unclassified. d ICD: International Classification of Diseases. e BERT: Bidirectional Encoder Representations from Transformers. f Not available. g Ada Boost: adaptive boosting. h XGBoost: extreme gradient boosting i SVM: support vector machine. j RF: random forest.

Atta Taseh, Souri Sasanfar, Michelle Chan, Evan Sirls, Ara Nazarian, Kayhan Batmanghelich, Jonathan F Bean, Soheil Ashkani-Esfahani

JMIR Med Inform 2025;13:e66973