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Welcome to Keele Repository

The Keele Repository is intended to be an Open Access showcase for the published research output of the university. Whenever possible, refereed documents accepted for publication, or finished artistic compositions presented in public, will be made available here in full digital format, and hyperlinks to standard published versions will be provided.



Latest Additions

Regulatory influence, board characteristics and climate change disclosures: evidence from environmentally sensitive firms in developing economy context (2024)
Journal Article

Purpose This study aims to examine the impact of board characteristics on climate change disclosures (CCDs) in the context of an emerging economy, with a unique focus on regulatory influences. Design/methodology/approach This study analyzes long... Read More about Regulatory influence, board characteristics and climate change disclosures: evidence from environmentally sensitive firms in developing economy context.

Exploring the feasibility and acceptance of an optimised physiotherapy approach for lateral elbow tendinopathy: a qualitative investigation within the OPTimisE trial (2024)
Journal Article

Objectives To explore the acceptability of an optimised physiotherapy (OPTimisE) intervention for people with lateral elbow tendinopathy (LET) and feasibility of comparing it to usual care in a randomised controlled trial. Design Semistructured in... Read More about Exploring the feasibility and acceptance of an optimised physiotherapy approach for lateral elbow tendinopathy: a qualitative investigation within the OPTimisE trial.

Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method (2024)
Journal Article

BackgroundTuberculosis is a principal public health issue. Reducing and controlling tuberculosis did not result in the expected success despite implementing effective preventive and therapeutic programs, one of the reasons for which is the delay in d... Read More about Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method.