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Welcome to OpenAIR@RGU

OpenAIR@RGU is the open access institutional repository of Robert Gordon University. It contains examples of research outputs produced by staff and research students, as well as related information about the university's funded projects and staff research interests. Further information is available in the repository policy. Any questions about submissions to the repository or problems with access to any of its content should be sent to the Publications Team at publications@rgu.ac.uk



Latest Additions

Information behaviour in high risk decision making: study of international postgraduates. (2022)
Journal Article
MCNICHOLAS, C. and MARCELLA, R. 2022. Information behaviour in high risk decision making: study of international postgraduates. Journal of information science [online], Online First. Available from: https://doi.org/10.1177/01655515221124080

This article explores the role of information in high risk consumer decision making. Forty-two qualitative interviews were undertaken with international non-EU postgraduates when making the high risk decision to study in a UK Business School. Prospec... Read More about Information behaviour in high risk decision making: study of international postgraduates..

The GP can't help me, there's no point bothering them: exploring the complex healthcare journeys of NHS workers in Scotland suffering from long COVID: a longitudinal study. (2022)
Presentation / Conference
ADAMS, N.N., MACIVER, E., KENNEDY, C., DOUGLAS, F., SKÅTUN, D., HERNANDEZ SANTIAGO, V., KYDD, A., TORRANCE, N. and GRANT, A. 2022. The GP can't help me, there's no point bothering them: exploring the complex healthcare journeys of NHS workers in Scotland suffering from long COVID: a longitudinal study. Presented at the 2022 Annual conference of the British Sociological Association Medical Sociology Study Group (BSA MedSoc 2022), 14-16 September 2022, Lancaster, UK.

Globally, Long COVID (LC) affects around 40% of people infected with COVID-19 (Chen et al, 2022). Despite high prevalence, symptoms are variable, and no clear healthcare pathway models exist for diagnosis and treatment. The Candidacy Framework descri... Read More about The GP can't help me, there's no point bothering them: exploring the complex healthcare journeys of NHS workers in Scotland suffering from long COVID: a longitudinal study..

Interpreting magnitude of change in strength and conditioning: effect size selection, threshold values and Bayesian updating. [Dataset] (2022)
Dataset
SWINTON, P.A., BURGESS, K., HALL, A., GREIG, L., PSYLLAS, J., ASPE, R., MAUGHAN, P. and MURPHY, A. [2022]. Interpreting magnitude of change in strength and conditioning: effect size selection, threshold values and Bayesian updating. [Dataset]. Journal of sports sciences [online], (accepted).

This is the supplementary data for the journal article: SWINTON, P.A., BURGESS, K., HALL, A., GREIG, L., PSYLLAS, J., ASPE, R., MAUGHAN, P. and MURPHY, A. [2022]. Interpreting magnitude of change in strength and conditioning: effect size selection, t... Read More about Interpreting magnitude of change in strength and conditioning: effect size selection, threshold values and Bayesian updating. [Dataset].

Interpreting magnitude of change in strength and conditioning: effect size selection, threshold values and Bayesian updating. (2022)
Journal Article
SWINTON, P.A., BURGESS, K., HALL, A., GREIG, L., PSYLLAS, J., ASPE, R., MAUGHAN, P. and MURPHY, A. [2022]. Interpreting magnitude of change in strength and conditioning: effect size selection, threshold values and Bayesian updating. Journal of sports sciences [online], (accepted).

The magnitude of change following strength and conditioning (S&C) training can be evaluated comparing effect sizes to threshold values. This study conducted a series of meta-analyses and compiled results to identify thresholds specific to S&C, and cr... Read More about Interpreting magnitude of change in strength and conditioning: effect size selection, threshold values and Bayesian updating..

DRL-RNP: deep reinforcement learning-based optimized RNP flight procedure execution. (2022)
Journal Article
ZHU, L., WANG, J., WANG, Y., JI, Y. and REN, J. 2022. DRL-RNP: deep reinforcement learning-based optimized RNP flight procedure execution. Sensors [online], 22(17), article 6475. Available from: https://doi.org/10.3390/s22176475

The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of t... Read More about DRL-RNP: deep reinforcement learning-based optimized RNP flight procedure execution..