Learning Capacity Research Articles

The use of bedside case-based learning in the clinical practice of midwifery education in China.

This study aimed to investigate the impact of bedside case-based learning on critical thinking and capacity for self-directed learning in a group of midwifery students in China. Bedside teaching is a well-established educational tool to improve the clinical practice of medical, nursing, and midwifery students. A new pedagogical approach; bedside case-based learning (BCBL) is an interactive teaching approach involving small-group, student-educator discussion to describe the etiology and management of a patient case. This new approach has been gradually integrated into Chinese midwifery education programs to promote clinical problem-solving skills, knowledge application, teamwork, and collaboration. A quasi-experimental pre-test-post-test group design. This study used a quasi-experimental pre-test-post-test group design. A convenience sample of 67 third-year students majoring in midwifery at the university were prospectively enrolled in this study. Pre- and post-BCBL class surveys were conducted using the Self-Rating Scale of Self-Directed Learning (SRSSDL) and the Critical Thinking Disposition Inventory-Chinese Version (CTDI-CV) to assess changes in self-learning and critical thinking abilities before and after the intervention. Following bedside case-based learning, the total scores for self-directed learning capacity and critical thinking increased, although there were no statistically significant differences (P > 0.05). Although there were no statistically significant differences between pre- and post-test results, students' self-assessed scores in self-directed learning and critical thinking improved between pre- and post-test.

‘We listened and supported and depended on each other’: a qualitative study of how leadership influences implementation of QI interventions

BackgroundThere is growing recognition in the literature of the ‘Herculean’ efforts required to bring about change in healthcare processes and systems. Leadership is recognised as a critical lever for implementation...

Fostering professional capacity for blended teaching and learning

Blended learning (BL) in higher education (HE) has for decades been considered a transformative way for universities to reach a wider range of students, increasing equity and availability to meaningful learning experiences. However, there is little in the BL literature around teaching academics and instructional designers’ experiences and perspectives on BL. This case study explores collaborative redesign through the reflections of a team comprising an academic Unit Chair, Learning Designer, and a Sessional Lecturer. It illustrates how a collaborative BL redesign process strengthened a unit that in 2022 had been deemed to be unsuccessful, and built educator capability through professional learning (PL). The result was a pedagogical and learning shift, which led to the unit’s successful delivery for online and on campus students in 2023. The findings have implications for BL and insight into collaborative design team experiences offering value to the academic community at large and foregrounding the value of pursuing a ‘ground-up’ collaborative BL redesign model.

Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics

The axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression, with Transformer showing the strongest results, under the constraint that the sensitivity is no less than 90%, reflecting the sensitivity of SLNB. This highlights the feasibility of noninvasive macro-SLNM prediction using DL. Feature importance analysis revealed that patients with similar characteristics exhibited different nodal status predictions, indicating the need for additional predictors for further improvement.

Using an Automated Operant Conditioning Procedure to Test Colour Discrimination in Two Juvenile Piranhas, Pygocentrus nattereri: A Lesson on Failures and Pitfalls and How to Avoid Them

Most studies on the cognitive abilities of fish have focused on model organisms adopted in behavioural neuroscience. To date, little attention has been devoted to characiformes fish and we record a lack of cognitive investigation on the piranha. In this study, we conducted a preliminary set of experiments to assess whether red-bellied piranhas (Pygocentrus nattereri) can solve an automated operant conditioning task, specifically, a reversal learning task. In Experiment 1, the fish were required to discriminate between red and green, while in Experiment 2, they had to discriminate between white and yellow. In either case, we found no evidence of learning capacities with our protocol after extensive training exceeding one thousand trials overall. In Experiment 3, we simplified the learning task by using achromatic stimuli (black and white discrimination) and always presenting the reinforced stimulus on the same side of the tank (a combination of response learning and place learning). Subjects did learn how to discriminate between the colours, although no subject was able to reach the criterion in the subsequent reversal learning task, suggesting that piranhas may be limited in their cognitive flexibility. However, our training procedure may have been inefficient in addressing this issue. We outline some potential limitations of the current methodology to help to establish a more effective approach for investigating operant conditioning in this species.

Understanding an emerging maker community in India: Socio-demographic traits, motivations, and satisfaction factors

Makerspaces are becoming increasingly popular due to their capacity for hands-on learning, innovation, collaboration, and the democratisation of technology. While numerous studies have explored makerspace users, they have largely focused on contexts within the Global North or China, leaving a significant gap in understanding the demographics and experiences of individuals who frequent these spaces in the Global South. The paper addresses this gap by understanding the factors influencing the motivations and satisfaction levels of an emerging community of makerspace users in India. Employing a cross-sectional survey methodology, data from 51 participants were collected via an online questionnaire and analysed using descriptive statistics and multivariate linear regression. Key findings indicate that access to specialised tools, expert guidance, and opportunities for personal and professional development are significant motivators for makerspace usage. Users expressed high satisfaction with the learning opportunities and resources provided. The study concludes that makerspaces play a crucial role in supporting innovation and professional growth in the Indian context, suggesting that policymakers and educators should consider these insights to enhance the effectiveness and reach of makerspaces, thus promoting broader innovation ecosystems. Future research should aim to include a more diverse participant pool to validate and expand upon these findings.

Continual compression model for online continual learning

Task-Free Continual Learning (TFCL) presents a notably demanding but realistic ongoing learning concept, aiming to address catastrophic forgetting in sequential learning systems. In this paper, we tackle catastrophic forgetting by introducing an innovative dynamic expansion framework designed to adaptively enhance the model’s capacity for novel data learning while also remembering the information learnt in the past, by using a minimal-size processing architecture. Our proposed framework incorporates three key mechanisms to mitigate model’ forgetting: (1) by employing a Maximum Mean Discrepancy (MMD)-based expansion mechanism that assesses the disparity between previously acquired knowledge and that from the new training data, serving as a signal for the model’s architecture expansion; (2) a component discarding mechanism that eliminates components characterized by redundant information, thereby optimizing the model size while fostering knowledge diversity; (3) a novel training sample selection strategy that leads to the diversity of the training data for each task. We conduct a series of TFCL experiments that demonstrate the superiority of the proposed framework over all baselines while utilizing fewer components than alternative dynamic expansion models. The results on the Split Mini ImageNet dataset, after splitting the original dataset into multiple tasks, are improved by more than 2% when compared to the closest baseline.

Exploring links between visuospatial ability and anatomy learning in education: A bibliometric analysis and scientific mapping

The field of medical education faces a significant challenge in teaching human anatomy, a fundamental aspect of medical science that is often perceived as complex and demanding. Understanding the shapes and anatomical structure locations is crucial for students to master the intricacies of anatomy. Visuospatial ability, which involves comprehending three-dimensional structures and their spatial relationships, plays a pivotal role in anatomy learning. The aim of this study was to examine keywords, territories/countries, the number of publications, institutions, authors, and associated journals related to visuospatial ability in anatomy learning through extensive bibliometric analysis. Data were gathered from the Scopus database, and VOSviewer was employed to conduct a bibliometric analysis. Approximately 552 eligible articles were examined in their entirety. The findings indicated that the most significant research growth occurred in 2012, with the United States of America emerging as the leading country in the field. Furthermore, Anatomical Science Education was recognized as the most esteemed journal in this field, with Wilson, T.D. being credited as the most influential author. The bibliometric data also demonstrated various approaches to improve visuospatial capacity in anatomy learning, such as utilizing virtual reality or other computer-assisted learning modalities and their integration into medical education practice. In conclusion, these findings highlight the critical role of visuospatial ability in facilitating the acquisition of anatomical knowledge and offer direction for future research in this area.

The Deficiency of the ASD-Related Gene CHD8 Disrupts Behavioral Patterns and Inhibits Hippocampal Neurogenesis in Mice.

Chromodomain helicase DNA-binding 8 (CHD8) is a gene that poses a high risk for autism spectrum disorder (ASD) and neurological development delay. Nevertheless, the impact of CHD8 haploinsufficiency on both hippocampus neurogenesis and behavior remains uncertain. Here, we performed behavioral assessments on male and female CHD8 heterozygous mice. The study discovered that both male and female CHD8 heterozygous mice displayed an impairment in preference for social novelty. Concurrently, CHD8 heterozygous mice exhibited anxiety-like behavior. However, its cognitive capacity for learning and memory is within the expected range. Furthermore, we discovered a reduction in the number of both immature and mature new neurons in mice with CHD8 heterozygous, resulting in an impeded neurogenesis process in the hippocampus. Taken together, our findings indicate that CHD8 plays a crucial role in the regulation of hippocampal neurogenesis, and further suggest that ASD-like behaviors observed in CHD8 heterozygous mice may be associated with disruptions in hippocampal neurogenesis.

Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression

Interrupted sampling repeater jamming (ISRJ) is a category of coherent jamming that greatly influences radars' detection performance. Since the ISRJ has greater power than true targets, ISRJ signals can be removed in the time domain. Due to frequency band loss, grating lobes will be produced if pulse compression (PC) is performed directly, which may generate false targets. Compressive sensing (CS) is an effective method to restore the original PC signal. However, it is challenging for classic CS approaches to manually select the optimization parameters (e.g., penalty parameters, step sizes, etc.) in different ISRJ backgrounds. In this article, a network method based on the Alternating Direction Method of Multipliers (ADMM), named ADMM-CSNet, is introduced to solve the problem. Based on the strong learning capacity of the deep network, all parameters in the ADMM are learned from radar data utilizing back-propagation rather than manually selecting in traditional CS techniques. Compared with classic CS approaches, a higher ISRJ removal signal restoration accuracy is reached faster. Simulation experiments indicate the proposal performs effectively and accurately for ISRJ removal signal reconstruction.

Climate-sensitive training for sustainable business in Dickson County with global applications

Purpose This paper aims to examine the implementation and impact of climate-sensitive training programs tailored to the specific climate risks and business needs of Dickson County, Tennessee. It aims to emphasize the importance of integrating climate adaptation strategies into business training and to propose a replicable model that can be adapted to regions facing similar challenges, contributing to global sustainability efforts. Design/methodology/approach The research uses Dickson County’s climate profile as a case study, analyzing its impact on local businesses in sectors such as agriculture, manufacturing and tourism. This paper integrates theoretical frameworks, including organizational learning and adaptive capacity, to provide practical strategies for climate-sensitive training. It further incorporates insights from local institutions, industry partnerships and public data, using a qualitative approach to analyze key themes related to training effectiveness and sustainability. Findings The findings demonstrate that climate-sensitive training significantly enhances the resilience and sustainability of businesses by preparing them to address local climate challenges. Critical factors for successful implementation include stakeholder engagement, industry-specific customization and the continuous evaluation of training outcomes. The model presented shows promise for broader applicability in other regions, especially when localized climate data and collaborative efforts are integrated. Originality/value This paper provides a novel, region-specific approach to climate-sensitive training, distinguishing itself by combining theoretical insights with practical strategies for businesses. By focusing on localized adaptation and stakeholder collaboration, the model addresses an existing gap in the literature on climate adaptation training for smaller, climate-vulnerable regions. This work offers a replicable and scalable framework that contributes to the global discourse on sustainable development and climate resilience in business practices.

VLFATRollout: Fully transformer-based classifier for retinal OCT volumes

Background and Objective:Despite the promising capabilities of 3D transformer architectures in video analysis, their application to high-resolution 3D medical volumes encounters several challenges. One major limitation is the high number of 3D patches, which reduces the efficiency of the global self-attention mechanisms of transformers. Additionally, background information can distract vision transformers from focusing on crucial areas of the input image, thereby introducing noise into the final representation. Moreover, the variability in the number of slices per volume complicates the development of models capable of processing input volumes of any resolution while simple solutions like subsampling may risk losing essential diagnostic details. Methods:To address these challenges, we introduce an end-to-end transformer-based framework, variable length feature aggregator transformer rollout (VLFATRollout), to classify volumetric data. The proposed VLFATRollout enjoys several merits. First, the proposed VLFATRollout can effectively mine slice-level fore-background information with the help of transformer’s attention matrices. Second, randomization of volume-wise resolution (i.e. the number of slices) during training enhances the learning capacity of the learnable positional embedding (PE) assigned to each volume slice. This technique allows the PEs to generalize across neighboring slices, facilitating the handling of high-resolution volumes at the test time. Results:VLFATRollout was thoroughly tested on the retinal optical coherence tomography (OCT) volume classification task, demonstrating a notable average improvement of 5.47% in balanced accuracy over the leading convolutional models for a 5-class diagnostic task. These results emphasize the effectiveness of our framework in enhancing slice-level representation and its adaptability across different volume resolutions, paving the way for advanced transformer applications in medical image analysis. The code is available at https://github.com/marziehoghbaie/VLFATRollout/.

GDnet-IP: Grouped Dropout-Based Convolutional Neural Network for Insect Pest Recognition

Lightweight convolutional neural network (CNN) models have proven effective in recognizing common pest species, yet challenges remain in enhancing their nonlinear learning capacity and reducing overfitting. This study introduces a grouped dropout strategy and modifies the CNN architecture to improve the accuracy of multi-class insect recognition. Specifically, we optimized the base model by selecting appropriate optimizers, fine-tuning the dropout probability, and adjusting the learning rate decay strategy. Additionally, we replaced ReLU with PReLU and added BatchNorm layers after each Inception layer, enhancing the model’s nonlinear expression and training stability. Leveraging the Inception module’s branching structure and the adaptive grouping properties of the WeDIV clustering algorithm, we developed two grouped dropout models, the iGDnet-IP and GDnet-IP. Experimental results on a dataset containing 20 insect species (15 pests and five beneficial insects) demonstrated an increase in cross-validation accuracy from 84.68% to 92.12%, with notable improvements in the recognition rates for difficult-to-classify species, such as Parnara guttatus Bremer and Grey (PGBG) and Papilio xuthus Linnaeus (PXLL), increasing from 38% and 47% to 62% and 93%, respectively. Furthermore, these models showed significant accuracy advantages over standard dropout methods on test sets, with faster training times compared to four conventional CNN models, highlighting their suitability for mobile applications. Theoretical analyses of model gradients and Fisher information provide further insight into the grouped dropout strategy’s role in improving CNN interpretability for insect recognition tasks.

Educator capacity for online global collaborative learning: developing a framework

ABSTRACT Despite recent moves to online learning, online global collaborative learning does not seem to have been taken up as a daily practice by most educators in the K–12 context. Yet it is evident that early adopters are seeing the benefits of such practice, not only for students but for their own professional learning. The research described here uses single case-study methodology to investigate a small group of educators, located in a range of countries and with varied teaching experiences, who have identified the potential of global interactions. Data analysis, using manual and software-supported coding, identified three key influences on the educators’ personal pedagogies: their disposition to online learning, their approach to professional learning, and conceptual change. The findings were used to construct an online global collaborative learning framework, which has the potential to inform considerations about building educator capacity in digital pedagogical knowledge.

Assessment of farmers’ resilience to drought and its influencing components: the case of Fars Province, Iran

ABSTRACT Drought is a complex socio-natural hazard that increases in magnitude with climate changes. Fars province, in the south of Iran, as one of the agricultural poles of Iran, has experienced severe droughts in recent years. Therefore, the present descriptive-correlational research was conducted aiming at assessing farmers’ resilience to drought within the province. Using a structured questionnaire, 420 farmers were surveyed following a multi-stage cluster random sampling method. Results of the research demonstrate that the farmers’ social resilience levels to drought are slightly higher than the average. Furthermore, three independent variables including adaptive capacity, job satisfaction, and learning capacity have the highest positive and significant correlations with social resilience. In addition, the results of multiple regression analysis showed that seven variables including adaptive capacity, perceived social support, learning capacity, social capital, coping capacity, job satisfaction, and drought perception had positive and statistically significant effects on the farmers’ resilience to drought. When considered together, these seven predicting variables account for about 41% of the variance of the dependent variable. In general, the results of this research can be useful in better understanding the social aspects of agricultural societies and increasing planners’ awareness concerning farmers’ resilience to climatic and environmental challenges.

Oseltamivir phosphate (Tamiflu) alters neurobehavior of zebrafish larvae by inducing mitochondrial dysfunction

Antiviral drugs are widely used, yet their potential risks during early development, particularly within the central nervous system, remain contentious. Oseltamivir phosphate (OSE), a commonly prescribed antiviral, is increasingly detected in various environments. However, its toxicity to organisms and the underlying mechanisms are not well understood. In this study, we employed the zebrafish model to evaluate the developmental neurotoxic effects of OSE at environmentally and therapeutically relevant doses, through high-throughput behavioral analysis, in vivo two-photon imaging, transcriptomic sequencing, pharmacological intervention, and biochemical and molecular assays. Our results indicated that OSE exposure increased heart rate and induced pericardial edema in zebrafish larvae. Additionally, OSE-exposed larvae exhibited hyperactive behavior, impaired social interactions, and reduced habitual learning capacity. Although OSE at our selected levels did not significantly affect neuron count in the brain, it activated neuroinflammatory responses, altered blood vessel morphology, modulated neurotransmitter levels and the expression of neurodevelopment-related genes. Transcriptomic analysis revealed upregulation of mitochondria-related genes associated with oxidative phosphorylation. Further assessments of mitochondrial function demonstrated altered activities of respiratory chain complexes, reduced mitochondrial membrane potential (MMP), and decreased ATP content. Notably, co-treatment with mitochondrial protectants acetyl-l-carnitine-hydrochloride (ALC) or nicotinamide riboside (NR) effectively mitigated OSE-induced neurobehavioral disorders. These findings suggest that overuse of OSE can pose neurodevelopmental risks for both humans and animals, potentially attributable to mitochondrial dysfunction.

The relationship between peer social support and self-regulated learning in Samarinda Ulu state junior high school students

Purpose: The capacity for self-regulated learning (SRL) assumes a pivotal role among state junior high school students (SMPN), necessitating autonomy in learning endeavors due to the evolving nature of educational processes. Social contextual factors, especially peer social support (PSS), exert significant influence on the manifestation of self-regulated learning. This study aims to elucidate the association between peer social support and self-regulated learning among students of state junior high schools in Samarinda Ulu. Method: Employing quantitative correlational research methodology. Random sampling techniques were employed, with sample sizes adjusted to ensure representation across schools, resulting in a cohort of 347 participants from seven state junior high schools within the Samarinda Ulu sub-district. Research instruments included the Peer Support Questionnaire scale and measures of self-regulated learning. Statistical analysis, using the Pearson Product Moment test aided by the Jamovi application. Result: Revealed a significant correlation with a p-value of 0.001 < 0.05 and a correlation coefficient of r = 0.399, This shows that the hypothesis in this study is proven, showing that there is a relationship between PSS and SRL. This value shows a positive number, which means there is a positive but relatively low relationship between the SRL variable and the PSS variable. The meaning can be interpreted as if the higher the PSS, the SRL possessed by students will also be classified as high. Then conversely, if the PSS level obtained is lower, then the SRL possessed by students will also be classified as low. Conclusion: Based on the results of the Pearson product moment correlation test, ap value of 0.001 <0.05 and an r value = 0.399 was obtained, which means that the working hypothesis (Ha) in this study was accepted, namely that there is a significant relationship between peer social support and self-regulated learning, the nature of the correlation is positive, which means that an increase in the score on the peer support variable will cause an increase in the SLR score. However, the magnitude of the correlation is relatively low, where the variance in peer social support that can explain the SLR variance is 15.92%.

Ouabain Ameliorates Alzheimer’s Disease-Associated Neuropathology and Cognitive Impairment in FAD4T Mice

Background: Alzheimer’s disease (AD) is a common clinical neurodegenerative disorder, primarily characterized by progressive cognitive decline and behavioral abnormalities. The hallmark pathological changes of AD include widespread neuronal degeneration, plaques formed by the deposition of amyloid β-protein (Aβ), and neurofibrillary tangles (NFTs). With the acceleration of global aging, the incidence of AD is rising year by year, making it a major global public health concern. Due to the complex pathology of AD, finding effective interventions has become a key focus of research. Ouabain (OUA), a cardiac glycoside, is well-known for its efficacy in treating heart disease. Recent studies have also indicated its potential in AD therapy, although its exact mechanism of action remains unclear. Methods: This study integrates bioinformatics, multi-omics technologies, and in vivo and in vitro experiments to investigate the effects of OUA on the pathophysiological changes of AD and its underlying molecular mechanisms. Results: This study analyzed the expression of the triggering receptor expressed on myeloid cells 2 (TREM2) across different stages of AD using bioinformatics. Serum samples from patients were used to validate soluble TREM2 (sTREM2) levels. Using an Aβ1-42-induced microglial cell model, we confirmed that OUA enhances the PI3K/AKT signaling pathway activation by upregulating TREM2, which reduces neuroinflammation and promotes the transition of microglia from an M1 proinflammatory state to an M2 anti-inflammatory state. To evaluate the in vivo effects of OUA, we assessed the learning and memory capacity of FAD4T transgenic mice using the Morris water maze and contextual fear conditioning tests. We used real-time quantitative PCR, immunohistochemistry, and Western blotting to measure the expression of inflammation-associated cytokines and to assess microglia polarization. OUA enhances cognitive function in FAD4T mice and has been confirmed to modulate microglial M1/M2 phenotypes both in vitro and in vivo. Furthermore, through bioinformatics analysis, molecular docking, and experimental validation, TREM2 was identified as a potential target for OUA. It regulates PI3K/Akt signaling pathway activation, playing a crucial role in OUA-mediated M2 microglial polarization and its anti-inflammatory effects in models involving Aβ1-42-stimulated BV-2 cells and FAD4T mice. Conclusions: These findings indicate that OUA exerts anti-neuroinflammatory effects by regulating microglial polarization, reducing the production of inflammatory mediators, and activating the PI3K/Akt signaling pathway. Given its natural origin and dual effects on microglial polarization and neuroinflammation, OUA emerges as a promising therapeutic candidate for neuroinflammatory diseases such as AD.

Blended Learning: What Changes?

This article questions the changes brought about in the teaching system of university courses after the COVID-19 pandemic. Online learning, once considered an experimental and emergency tool, is increasingly becoming a standard practice integrated into teaching delivery systems. This article examines the impact that blended learning has on the host city and how it is perceived and experienced by the key stakeholders. Focusing on two degree courses that employ a blended learning model, the article proposes a survey for teachers and students to investigate the problems and advantages of blended courses. In both groups, there is no consensus of opinion or a clear trend on any issue. This fragmentation of responses should be understood as stemming from the individual motivations, unique characteristics, and personal experiences of each participant, intertwined with their university careers. It also reflects the level of adaptation to blended teaching achieved by each individual. It becomes clear that blended teaching encompasses all the challenges of online teaching but also expands access to university education and offers the possibility of exploring the educational potential offered by technology. Blended learning encourages students to be more independent and to develop their capacity for self-directed learning, though not all students are prepared for this shift. For teachers, blended learning also demands a methodological shift, differentiating between online and in-person lessons. However, while challenges remain, the progress made so far appears promising for the future. To ensure continued success, it is essential to focus on improving internet access, strengthening students’ self-learning abilities, and simultaneously enhancing teachers’ competencies in using digital tools and facilitating remote teaching.

Relationship between subjective cognitive functioning and fluid and crystallized cognitive abilities in bipolar disorder.

People with bipolar disorder (BD) often show inaccurate subjective ratings of their objective cognitive function. However, it is unclear what information individuals use to formulate their subjective ratings. This study evaluated whether people with BD are likely using information about their crystallized cognitive abilities (which involve an accumulated store of verbal knowledge and skills and are typically preserved in BD) or their fluid cognitive abilities (which involve the capacity for new learning and information processing in novel situations and are typically impaired in BD) to formulate their subjective cognitive ratings. Eighty participants diagnosed with BD and 55 control volunteers were administered cognitive tests assessing crystallized and fluid cognitive abilities. Subjective cognitive functioning was assessed with the Cognitive Failures Questionnaire (CFQ), daily functioning was rated using the Multidimensional Scale of Independent Functioning (MSIF) and the Global Assessment of Functioning Scale (GAF), and quality of life was assessed with the Quality of Life in Bipolar Disorder scale (QoL.BD). The BD group exhibited considerably elevated subjective cognitive complaints relative to controls. Among participants with BD, CFQ scores were associated with fluid cognitive abilities including measures of memory and executive function, but not to crystallized abilities. After controlling for objective cognition and depression, higher cognitive complaints predicted poorer psychosocial outcomes. Cognitive self-reports in BD may represent a metacognitive difficulty whereby cognitive self-appraisals are distorted by a person's focus on their cognitive weaknesses rather than strengths. Moreover, negative cognitive self-assessments are associated with poorer daily functioning and diminished quality of life.