ORCID Profile
0000-0002-2526-4649
Current Organisations
University College Dublin
,
Rigshospitalet
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Publisher: Elsevier BV
Date: 2011
DOI: 10.1016/J.RIDD.2010.09.016
Abstract: An experiment investigated the effects of different levels of task complexity in pre-training on over-selectivity in a subsequent match-to-s le (MTS) task. Twenty human participants were ided into two groups exposed either to a 3-element, or a 9-element, compound stimulus as a s le during MTS training. After the completion of training, both groups were tested on an MTS task using a novel 6-element compound s le stimulus. The level of over-selectivity at test was influenced by the training. Specifically, the group exposed to a more complex (9-element) training task displayed higher levels of over-selectivity at test than the group with a less complex training task. The results suggest that over-selectivity may be a learned response to complex situations, and are discussed with respect to theories and treatments for over-selectivity.
Publisher: SAGE Publications
Date: 26-08-2021
DOI: 10.1177/02654075211034236
Abstract: Following the global outbreak of COVID-19 in March 2020, in iduals report psychological distress associated with the “new normal”—social distancing, financial hardships, and increased responsibilities while working from home. Given the interpersonal nature of stress and coping responses between romantic partners, based on the systemic transactional model this study posits that perceived partner dyadic coping may be an important moderator between experiences of COVID-19 psychological distress and relationship quality. To examine these associations, self-report data from 14,020 people across 27 countries were collected during the early phases of the COVID-19 pandemic (March–July, 2020). It was hypothesized that higher symptoms of psychological distress would be reported post-COVID-19 compared to pre-COVID-19 restrictions (Hypothesis 1), reports of post-COVID-19 psychological distress would be negatively associated with relationship quality (Hypothesis 2), and perceived partner DC would moderate these associations (Hypothesis 3). While hypotheses were generally supported, results also showed interesting between-country variability. Limitations and future directions are presented.
Publisher: Elsevier BV
Date: 11-2022
DOI: 10.1016/J.CMPB.2022.107125
Abstract: Within medical research, cure models are useful for analyzing time-to-event data in the scenario where a proportion of the analyzed in iduals are expected to never experience the event of interest. Cure models are also useful for modelling the relative survival in scenarios where a proportion of the in iduals are expected to eventually experience a mortality rate similar to that of the general population. Here we present two R packages, cuRe and rstpm2, that provide researchers with several tools for performing statistical inference using parametric cure models. Cure models are commonly used to estimate 1) the proportion of in iduals that are cured and 2) the event-time distribution of in iduals who are not cured. This can be done using simple parametric distributions for the event-time distribution of the uncured, but our implementations also enable fitting of more flexible spline-based cure models. The parametric framework of both packages ensures that cure models for the relative survival can easily be used. The cuRe package contains two main functions for estimating parametric mixture cure models one based on simple parametric distributions (e.g. Weibull or exponential) and one utilizing a spline-based formulation of the cure model. The rstpm2 package enables estimation of spline-based latent cure models, i.e., cure models with no explicit parameters modelling the proportion of cured in iduals. Through the R-packages cuRe and rstpm2, a wide range of different parametric cure models can be fitted. The cuRe package also contains a number of useful post-estimation procedures for computing the time to statistical cure and conditional probability of cure, which may spread the use of cure models in medical research.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Louise McHugh.