Psychological Aspects of Nerve Gap Reconstruction: Addressing Patient Perspectives and Expectations.

Purpose: Preoperative expectations play a major role in determining patient satisfaction after surgery. The aim of this study was to characterize patient's preoperative expectations and postoperative perceptions of nerve gap repair surgery. Methods: We conducted a search of Embase, Scopus, and Web of Science databases for peer-reviewed articles that studied patient expectations, perceptions, and impressions of nerve gap repair in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies related to lumbar plexus radiculopathy, reimplantation, or patient satisfaction scores without patient testimony were excluded. Primary and secondary outcomes were patient's preoperative expectations and postoperative perceptions of nerve gap repair surgery, respectively. Results: We included 11 studies evaluating a total of 462 patients. One study evaluated only patient expectations, six studies evaluated only patient perspectives, and four studies evaluated both. Patients were generally overly optimistic in their expectations of surgery. Postoperative satisfaction ranged from 82% to 86%, and 81% to 87% of patients would choose to undergo their surgery again knowing what they know now. Conclusions: Patient expectations in nerve gap repair are optimistic, and at times unrealistic. Patient satisfaction with nerve gap repair is high and subject to influence from preoperative education and postoperative outcomes of functional and sensory recovery. Clinical relevance: Surgeons should be aware that patient expectations of their postoperative outcomes can have substantial impacts on their perceived management and overall satisfaction. More emphasis should be placed on preoperative education and expectation management to optimize patient satisfaction.

Parents' and children's experiences of participating in a randomized controlled clinical trial: AIDIT-QS.

Participation in a paediatric, complex randomized controlled trial (RCT) might add to the family burden when a child is diagnosed with a severe disease. Although important, there are only a limited number of papers describing this aspect of research from the family point of view. This study explored parents' and children's experiences of participation in a research study shortly after the child had been diagnosed with type 1 diabetes. Sixteen parents (nine mothers, seven fathers) and nine children were interviewed by an independent researcher about their inducement, the decision-making process within the family which led to their participation, and their experience of having done so. The result showed that the parents wanted to contribute to improve treatment for children with diabetes in general but also specifically for their own child. Older children were more involved in the decision making than the younger children. Study information needs to be communicated clearly and effectively since decision-making based on information of a clinical trial directly after the child's diabetes onset proved difficult. Being randomized to the intervention group in this specific study was considered somewhat burdensome. However, parental participants in both intervention and control group claimed that they would recommend participation in research studies to other parents in a similar situation, and so did the children. There was no difference between the mothers' and fathers' experiences.

Parents' expectations: A predominant driving force for the parents was the expectation that the study outcome could lead to something good for both their own child and other children with type 1 diabetes.Children's perspective on participation: Older children appreciated being involved in the decision-making process and valued their role in potentially helping others with diabetes. However, younger children were less involved and often relied on their parents for decision-making.Personal benefits: Both children and parents considered it important to gain something for themselves; by participating, they could benefit from more advanced technology and more rigorous follow-ups.Importance of control group: It was important for the families' motivation for completing the study that the researchers conveyed that the control group was as important for the outcome of the study as the intervention group.Future treatments: The parents felt that participation in the clinical trial could eventually lead to new treatments that could help their own child.Perceived safety: The fact that the clinical trial was considered well-planned and safe and implied no risk for the child made it easier to agree to participation.Effective communication: Since the onset of diabetes is emotionally stressful, and diabetes treatment itself is demanding, effective communication about the content of such a clinical trial is necessary, otherwise families may not understand what they are agreeing to.Burden on the intervention group: This clinical trial was somewhat burdensome for the intervention group to participate in; nevertheless, all of the families remained committed to their reasons for participating and completed the study.

Expectations concerning cancer treatment: perspectives of medical oncologists and patients on advanced, unresectable lung carcinoma.

Introduction: This study seeks to compare expectations regarding systemic cancer treatment for advanced lung cancer from the perspectives of both patient and medical oncologist. Methods: A cross-sectional study involving 17 medical oncologists from 13 Spanish hospitals between 2021 and 2022. Patients with advanced, unresectable lung cancer were recruited prior to initiating systemic cancer treatment. Both patients and oncologists completed the NEOetic-EIT and the STAR. Results: Seventeen medical oncologists specializing in lung cancer participated, with a mean age of 36.2 years (range 28-56); 65% were female. The study included 298 patients with advanced, unresectable lung cancer, predominantly non-small cell type (72%), and most at stage IV (77%). Most patients were retired or unemployed (71%), and married or partnered (77%). Treatment approaches varied, with 44% based on biomarkers. Oncologists had greater expectations of positive outcomes for participants with better baseline prognosis, such as ECOG 0, newly diagnosed, locally advanced, unresectable non-small cell lung cancer, and those receiving biomarker-based treatments. In contrast, patients' treatment expectations did not vary based on sociodemographic or clinical factors. Generally, patients had high expectations of cure, in contrast to oncologists' lower expectations, though both anticipated similar quality-of-life improvements. Patients anticipated more side effects than oncologists. Among oncologists, expectations varied by gender and decreased with age and experience, with no differences detected among patients based on gender, age, or doctor-patient relationship. Conclusion: This study reveals the complex expectations of patients and oncologists in advanced lung cancer treatment. It underscores the need for effective communication in oncology to align patient expectations with clinical realities.

A new factor analysis model for factors obeying a Gamma distribution.

The traditional factor analysis model assumes that the factors obey a normal distribution, which is not appropriate in fields whose data are nonnegative. For this kind of problem, we construct a more practical factor model, assuming that the factors obey a Gamma distribution. We develop a new factor analysis model and discuss its true loading matrix. Then we study its parameter estimation with the maximum likelihood estimation (MLE) method based on an Expectation-Maximization (EM) algorithm, where step E is realized by the Metropolis-Hastings (M-H) algorithm in the Markov Chain Monte Carlo (MCMC) method. We use the new model to empirically study real data, and evaluate its information extraction ability, using the defined true loading matrix to calculate the true loading of the factor. We compare the new model and traditional factor analysis models on simulated and real data, respectively, whose results show that the new model has better information extraction ability for nonnegative data when the number of factors is the same.

The Impact of AI Usage on University Students' Willingness for Autonomous Learning.

As artificial intelligence (AI) technology becomes increasingly integrated into education, understanding the theoretical mechanisms that drive university students to adopt new learning behaviors through these tools is essential. This study extends the Expectation-Confirmation Model (ECM) by incorporating both cognitive and affective variables to examine students' current AI usage and their future expectations. The model includes intrinsic and extrinsic motivations, focusing on three key factors: positive emotions, digital efficacy, and willingness for autonomous learning. A survey of 721 valid responses revealed that positive emotions, digital efficacy, and satisfaction significantly influence continued AI usage, with positive emotions being particularly critical. Digital efficacy and perceived usefulness also impact satisfaction, but long-term usage intentions are more effectively driven by positive emotions. Furthermore, digital efficacy strongly affects the willingness for autonomous learning. Therefore, higher education institutions should promote AI technology, enhance students' expectation-confirmation levels, and emphasize positive emotional experiences during AI use. Adopting a "human-machine symbiosis" model can foster active learning, personalized learning pathways, and the development of students' digital efficacy and innovation capabilities.

Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects.

BACKGROUND: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, is limited. OBJECTIVES: The aim of this work is to develop a binary-outcome mixed-effects Xgboost (BME) model that integrates random effects at the hospital level. To ascertain how well the model handles correlated data in cardiovascular outcomes, we aim to assess its performance and compare it to fixed-effects Xgboost and traditional logistic regression models. METHODS: A total of 227,087 patients over 17 years of age, undergoing cardiac surgery from 42 UK hospitals between 1 January 2012 and 31 March 2019, were included. The dataset was split into two cohorts: training/validation (n = 157,196; 2012-2016) and holdout (n = 69,891; 2017-2019). The outcome variable was 30-day mortality with hospitals considered as the clustering variable. The logistic regression, mixed-effects logistic regression, Xgboost and binary-outcome mixed-effects Xgboost (BME) were fitted to both standardized and unstandardized datasets across a range of sample sizes and the estimated prediction power metrics were compared to identify the best approach. RESULTS: The exploratory study found high variability in hospital-related mortality across datasets, which supported the adoption of the mixed-effects models. Unstandardized Xgboost BME demonstrated marked improvements in prediction power over the Xgboost model at small sample size ranges, but performance differences decreased as dataset sizes increased. Generalized linear models (glms) and generalized linear mixed-effects models (glmers) followed similar results, with the Xgboost models also excelling at greater sample sizes. CONCLUSIONS: These findings suggest that integrating mixed effects into machine learning models can enhance their performance on datasets where the sample size is small.

Distinguishing expectation and attention effects in processing temporal patterns of visual input.

The current study investigated how the brain sets up expectations from stimulus regularities by evaluating the neural responses to expectations driven implicitly (by the stimuli themselves) and explicitly (by task demands). How the brain uses prior information to create expectations and what role attention plays in forming or holding predictions to efficiently respond to incoming sensory information is still debated. We presented temporal patterns of visual input while recording EEG under two different task conditions. When the patterns were task-relevant and pattern recognition was required to perform the button press task, three different event-related brain potentials (ERPs) were elicited, each reflecting a different aspect of pattern expectation. In contrast, when the patterns were task-irrelevant, none of the neural indicators of pattern recognition or pattern violation detection were observed to the same temporally structured sequences. Thus, results revealed a clear distinction between expectation and attention that was prompted by task requirements. These results provide complementary pieces of evidence that implicit exposure to a stimulus pattern may not be sufficient to drive neural effects of expectations that lead to predictive error responses. Task-driven attentional control can dissociate from stimulus-driven expectations, to effectively minimize distracting information and maximize attentional regulation.

On the interpretability of the SVM model for predicting infant mortality in Bangladesh.

BACKGROUND: Although machine learning (ML) models are well-liked for their outperformance in prediction, greatly avoided due to the lack of intuition and explanation of their predictions. Interpretable ML is, therefore, an emerging research field that combines the performance and interpretability of ML models to create comprehensive solutions for complex decision-making analysis. Conversely, infant mortality is a global public health concern affecting health, social well-being, socio-economic development, and healthcare services. The study employs advanced interpretable ML techniques to anticipate and understand the factors affecting infant mortality in Bangladesh, overcoming the shortcomings of the conventional logistic regression (LR) model. METHODS: By utilizing the global surrogate model and local individual conditional expectation (ICE) interpretability technique, the interpretable support vector machine (SVM) has been used in this study to reveal significant characteristics of infant mortality using data from the Bangladesh Demographic and Health Survey (BDHS) 2017-18. To investigate intricate decision-making analysis of infant mortality, we adapted SVM and LR techniques with the hyperparameter tuning parameters. These models' performances were initially assessed using the receiver operating characteristics (ROC) curve, run-time, and confusion matrix parameters with 100 permutations. Afterward, the SVM model's model-agnostic explanation and the LR model's interpretation were compared to enhance advanced comprehension for further insights. RESULTS: The results of the 100 permutations demonstrated that the LR model (Average: accuracy = 0.9105, precision = NaN, sensitivity = 0, specificity = 1, F1-score = 0, area under the ROC curve (AUC) = 0.6780, run-time = 0.0832) outperformed the SVM model (Average: accuracy = 0.8470, precision = 0.1062, sensitivity = 0.0949, specificity = 0.9209, F1-score = 0.1000, AUC = 0.5632, run-time = 0.0254) in predicting infant mortality, but the LR model had a slower run-time and it was unable to predict any positive cases. The interpretation of LR analysis revealed that infant mortality rates decrease when mothers give birth after over two years, with higher educational attainment, overweight or obese mothers, working mothers, and families with polluted cooking fuel having lower rates. The local ICE interpretability technique, which depicts individual influences on the average likelihood of dying within the first birthday, explores the interpretable SVM model that mothers with normal BMIs, giving birth within two years, using less polluted cooking fuel, working mothers, and having male infant were more likely to experience infant death. The interpretable SVM model based on the global surrogate model also reveals that working mothers who used polluted cooking fuel at home and working women who used less polluted cooking fuel but had a longer period between pregnancies than two years would have higher infant death rates. Even among non-working mothers who used polluted cooking fuel and gave birth within two years of the preceding one, infant death rates were higher. CONCLUSIONS: The interpretable SVM model reveals global interpretations help clinicians understand the entire conditional distribution, while local interpretations focus on specific instances, providing different insights into model behavior. Interpretable ML models aid policymakers, stakeholders, and families in understanding and preventing infant deaths by improving policy-making strategies and establishing effective family counseling services.

Communicated priors tune the perception of control.

Action allows us to shape the world around us. But to act effectively we need to accurately sense what we can and cannot control. Classic theories across cognitive science suppose that this 'sense of agency' is constructed from the sensorimotor signals we experience as we interact with our surroundings. But these sensorimotor signals are inherently ambiguous, and can provide us with a distorted picture of what we can and cannot influence. Here we investigate one way that agents like us might overcome the inherent ambiguity of these signals: by combining noisy sensorimotor evidence with prior beliefs about control acquired through explicit communication with others. Using novel tools to measure and model control decisions, we find that explicit beliefs about the controllability of the environment alter both the sensitivity and bias of agentic choices; meaning that we are both better at detecting and more biased to feel control when we are told to expect it. These seemingly paradoxical effects on agentic choices can be captured by a computational model where expecting to be in control exaggerates the sensitivity or 'gain' of the mechanisms we use to detect our influence over our surroundings - making us increasingly sensitised to both true and illusory signs of agency. In combination, these results reveal a cognitive and computational mechanism that allows public communication about what we can and cannot influence to reshape our private sense of control.

Adaptation patterns and their associations with mismatch negativity: An electroencephalogram (EEG) study with controlled expectations.

Adaptation refers to the decreased neural response that occurs after repeated exposure to a stimulus. While many electroencephalogram (EEG) studies have investigated adaptation by using either single or multiple repetitions, the adaptation patterns under controlled expectations manifested in the two main auditory components, N1 and P2, are still largely unknown. Additionally, although multiple repetitions are commonly used in mismatch negativity (MMN) experiments, it is unclear how adaptation at different time windows contributes to this phenomenon. In this study, we conducted an EEG experiment with 37 healthy adults using a random stimulus arrangement and extended tone sequences to control expectations. We tracked the amplitudes of the N1 and P2 components across the first 10 tones to examine adaptation patterns. Our findings revealed an L-shaped adaptation pattern characterised by a significant decrease in N1 amplitude after the first repetition (N1 initial adaptation), followed by a continuous, linear increase in P2 amplitude after the first repetition (P2 subsequent adaptation), possibly indicating model adjustment. Regression analysis demonstrated that the peak amplitudes of both the N1 initial adaptation and the P2 subsequent adaptation significantly accounted for variance in MMN amplitude. These results suggest distinct adaptation patterns for multiple repetitions across different components and indicate that the MMN reflects a combination of two processes: the initial adaptation in the N1 and a continuous model adjustment effect in the P2. Understanding these processes separately could have implications for models of cognitive processing and clinical disorders.

Breaking into the black box of customer perception towards robot service: Empirical evidence from service sector.

The advent of artificial intelligence and machine learning has enabled robots to serve in consumer market for a better customer experience. Nevertheless, acceptance of robotic technology among consumers is still lacking. Therefore, this study has developed an integrated model with robot appearance, expectation confirmation model, diffusion of innovation and theory of planned behavior and empirically investigates customer intention to use service robot. The research model is empirically tested with 349 responses retrieved from customers visiting retail stores. Statistical results have revealed that customer innovativeness, compatibility, behavioral control, expectation confirmation, service robot appearance and subjective norms explained R 2 80.1 % variance in customer attitude to use service robot. Practically, this research has suggested that policy makers should pay attention in innovativeness, compatibility, perceived behavioral control, expectation confirmation, robot appearance and subjective norms to boost robot service acceptance among customers. This study is original as it develops an integrated model with the combination robot appearance, theory of planned behavior, expectation confirmation and diffusion of innovation theory. In addition to that customer self-identity is conceptualized as moderating factor and hence distinguishing current research with past studies.

A unified EM framework for estimation and inference of normal ogive item response models.

Normal ogive (NO) models have contributed substantially to the advancement of item response theory (IRT) and have become popular educational and psychological measurement models. However, estimating NO models remains computationally challenging. The purpose of this paper is to propose an efficient and reliable computational method for fitting NO models. Specifically, we introduce a novel and unified expectation-maximization (EM) algorithm for estimating NO models, including two-parameter, three-parameter, and four-parameter NO models. A key improvement in our EM algorithm lies in augmenting the NO model to be a complete data model within the exponential family, thereby substantially streamlining the implementation of the EM iteration and avoiding the numerical optimization computation in the M-step. Additionally, we propose a two-step expectation procedure for implementing the E-step, which reduces the dimensionality of the integration and effectively enables numerical integration. Moreover, we develop a computing procedure for estimating the standard errors (SEs) of the estimated parameters. Simulation results demonstrate the superior performance of our algorithm in terms of its recovery accuracy, robustness, and computational efficiency. To further validate our methods, we apply them to real data from the Programme for International Student Assessment (PISA). The results affirm the reliability of the parameter estimates obtained using our method.

GAEM: Genetic Algorithm based Expectation-Maximization for inferring Gene Regulatory Networks from incomplete data.

In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic Algorithm and Expectation-Maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://github.com/parniSDU/GAEM.

Exploring Nursing Students' Expectations on Preceptoring and Preceptorship Program: A Mixed Method Study.

Introduction: This study addresses the imperative in contemporary nursing education to prepare students for diverse healthcare settings by exploring nursing students' expectations and perceptions of preceptorship programs, emphasizing the role of evidence-based educational strategies. The research aims to bridge the existing literature gap and contribute valuable insights into strategically designing preceptorship programs aligned with nursing students' needs, preferences, and aspirations, ultimately enhancing precepting practices and relationships within nursing education. Methods: Employing a sequential explanatory mixed method, 140 nursing students, from various colleges in the United Arab Emirates (UAE) participated in the study. A structured questionnaire, encompassing demographic information, a need assessment survey, and a survey on expectations on preceptors was administered. A focus group discussion was conducted to identify perceived barriers to the utilization of preceptorship practices in nursing colleges. Data analysis involved descriptive statistics, the chi-square test, exploratory factor analysis, and content analysis of the focus group discussion. Results: The majority of participants expressed a high need for a preceptorship program, providing empirical evidence to support the development of a nurse educator preceptorship program in colleges and institutes. Preceptorship was identified as a significant contributor to career growth and achievement for nursing students, serving as a valuable tool to establish professional competency throughout their careers. Conclusion: There is a complex demand and high expectations for the core role of nurse educators as preceptors in the field of nursing education. This pioneering study sheds light on the need and perception of nursing students for a preceptorship program in the nursing curriculum.

Effects of color-flavor association on visual search process for reference pictures on beverage packaging: behavioral, electrophysiological, and causal mechanisms.

The visual search for product packaging involves intricate cognitive processes that are prominently impacted by learned associations derived from extensive long-term experiences. The present research employed EEG technology and manipulated the color display of reference pictures on beverage bottles to explore the underlying neurocognitive pathways. Specifically, we aimed to investigate the influence of color-flavor association strength on the visual processing of such stimuli as well as the in-depth neural mechanisms. The behavioral results revealed that stimuli with strong association strength triggered the fastest response and the highest accuracy, compared with the stimuli with weak association strength and the achromatic ones. The EEG findings further substantiated that the chromatic stimuli evoked a more pronounced N2 component than achromatic ones, and the stimuli with strong association strength elicited larger P3 and smaller N400 amplitudes than the ones with weak association strength. Additionally, the source localization using sLORETA showed significant activations in the inferior temporal gyrus. In conclusion, our research suggests that (1) color expectations would guide visual search process and trigger faster responses to congruent visual stimuli, (2) both the initial perceptual representation and subsequent semantic representation play pivotal roles in effective visual search for the targets, and (3) the color-flavor association strength potentially exerts an impact on visual processing by modulating memory accessibility.

Out of sight, not out of mind: New pupillometric evidence on object permanence in a sample of 10- and 12-month-old German infants.

Findings on the emergence and interpretation of early object representation in the first year of life diverge widely between designs that employ looking times versus action-based measures. As a promising new approach, pupillometry has produced evidence for object permanence at 18 months of age, but not younger as of yet. In the current study, we (re)investigated object permanence following occlusion events in a pupillometric violation-of-expectation paradigm optimized for younger infants. During each trial, infants observed a toy object's occlusion and prompt reveal in the expected condition or its absence in the unexpected condition. Across two experiments, we show that 10- and 12-month-old infants' (total N = 82) pupils dilate in response to unexpected object disappearances relative to expected appearances. Control analyses revealed no differences between the scenes before the experimental manipulation, excluding perceptual interpretations. We further report an age-dependent effect of condition on pupil responses, with unexpected outcomes triggering greater pupil dilation in the older group. These results provide positive pupillometric evidence in support of object permanence in the context of a violation-of-expectation paradigm at 10 and 12 months of age.

Evaluating perceived value and expected value gaps based on patient experience during outpatient encounters: An empirical study in China.

Background: Improving the quality of care relies on understanding patients' perceptions and expectations based on their experiences. The study aimed to determine the gaps between patients' perceived value and expected value, and to identify critical areas for outpatient service improvement. Method: This cross-sectional study was conducted in China from November 2020 to February 2021. A sample of 572 outpatients, randomly selected from a comprehensive tertiary public hospital, was surveyed using a validated patient perceived value questionnaire. Importance-performance analysis was used to evaluate the differences between patients' perceived and expected value. Results: The scores of patients' expected value for outpatient services were significantly higher than their perceived value in all 29 items and 8 dimensions. The items with the highest and lowest gaps were "short waiting time" (-1.52) and "hospital reputation and popularity" (-0.24) respectively, and the dimensions of price and efficiency (functional value) were located in the quadrant of high expectation and low perception. Conclusion: Our findings are useful for hospital administrators and policymakers to identify strategic focus areas and allocate resources rationally and effectively. We suggest healthcare providers should take measures to narrow the gaps, especially in terms of service efficiency and price.

Patients' Experiences, Satisfaction and Quality of Life With Physiotherapy Follow-Up After Total Knee Arthroplasty: A Phenomenological Qualitative Study Protocol.

INTRODUCTION: Available quantitative studies indicate that patients who have undergone Total Knee Arthroplasty (TKA) experience dissatisfaction. There is a lack of comprehensive information on the experiences, satisfaction, and quality of life of patients after TKA. Hence, there is a need to explore the patients' perspective about the satisfaction and experiences undergoing rehabilitation and to explore the factors influencing quality of life with physiotherapy after 1st and 3rd months of TKA. METHODS: 35 patients with post TKA, aged from 45 years, will participate in semi-structured face-to-face or telephone interviews. The participants will be recruited using criterion-based purposive sampling. The interviews will be audio-recorded and transcribed. NVivo 14V software and Braun and Clarke thematic analysis will be used. Credibility, transferability, dependability, and confirmability will be ensured. RESULTS: The transcribed verbatim transcript will be analysed to generate sub-themes and themes using thematic analysis. Irrespective of the responses received from male or female patients' data would be analysed using inductive qualitative analysis to explore their perspectives. CONCLUSION: This study is the first qualitative study from lower- and middle-income country that aims to investigate the satisfaction and experiences of patients after undergoing TKA rehabilitation. The efficacy of the data and subsequent suggestions would rely on the insights generated from the qualitative study, which would support the rehabilitation of patients in their later years.

Awareness and Underlying Factors for Total Knee Arthroplasty Reluctance in Saudi Arabia.

Background Total knee arthroplasty (TKA) is a successful surgical intervention for advanced knee arthritis. The efficacy of TKA in reducing pain and restoring joint function has been well documented. Despite the rewarding outcomes of TKA for knee osteoarthritis patients, their willingness to consider the procedure is limited. Aim This study aimed to assess patients' awareness and knowledge of total knee arthroplasty benefits and complications. Further, the reasons and factors contributing to reluctance among orthopedic patients in Saudi Arabia should be determined. Methods An online, structured, and self-administered questionnaire was used to collect data from adult orthopedic patients of both genders who were reluctant to undergo total knee arthroplasty despite surgeons' recommendations. The online questionnaire link was shared across multiple platforms, orthopedic forums, and healthcare social media channels. Qualitative data were presented as frequencies and percentages, while continuous data were reported as the mean (standard deviation [SD]). The statistical package for the social sciences software program was used for statistical analysis. Results A total of 629 participants were involved. The awareness of the expected benefits score, on a scale from 7 to 35, showed a mean (SD) of 20.9 (5.6). The score of the attitude towards expected complications, on a scale from 5 to 25, had a mean (SD) of 15.2 (3.6). The attitude towards the expected complications showed a significantly higher mean (SD) score in the older group aged >60 years than the younger one aged <40 years (15.7 (4.1) vs. 14.9 (3.5), respectively). Likewise, overweight and obese participants showed a significantly higher mean (SD) expected complications score compared to the healthy and underweight ones (15.4 (3.7) vs. 14.8 (3.5), respectively). The recorded reasons for refusal to undergo TKA were fear of anesthesia complications (317, 50.4%), followed by financial limitations (245, 39.0%), the unavailability of experienced surgeons (232, 36.9%), and fear of unfavorable outcomes (189, 30.0%). Conclusion There was a gap in knowledge and awareness of total knee arthroplasty among orthopedic patients in Saudi Arabia. Perceptions of benefits were inadequate, and there were misconceptions about the expected complications. The level of expected complications was higher among elderly and obese patients. Furthermore, fear of anesthesia complications and unfavorable outcomes, in addition to economic and financial problems, constituted major barriers to undergoing the procedure.

A Longitudinal Study of the Impact of COVID-19 on Optimism Prediction.

The purpose of this longitudinal study was to investigate how optimistic predictions, hopelessness, and depressive symptoms changed as a result of the COVID-19 pandemic, and the causal relationships between these variables. To achieve this purpose, we used data from online surveys conducted in 2019 and 2021 among men and women aged 20-79. Based on item response theory, we developed a future prediction task for the assessment of optimistic predictions. Our comparison of online survey responses found a decline in optimistic predictions before and after the pandemic. More specifically, there were no change in predictions of negative future events, but there was the decrease in predictions of positive future events. Furthermore, we found that those who were more stressed by COVID-19 were less likely to have an optimistic view of the near future. We also found a relationship between optimistic predictions and hopelessness and depressive symptoms with lower optimism predicting more hopelessness and more depression predicting lower optimism. To prevent feelings of hopelessness, it is important to help people develop positive expectations about the future.