Chronic stroke survivors underestimate their upper limb motor ability in a simple 2D motor task.

BACKGROUND: Stroke survivors can exhibit a mismatch between the actual motor ability of their affected upper limb and how much they use it in daily life. The resulting non-use of the affected upper limb has a negative impact on participation in neurorehabilitation and functional independence. The factors leading to non-use of the affected upper limb are poorly understood. One possibility is that non-use comes about through inappropriately low confidence in their own upper limb motor abilities. OBJECTIVE: We asked whether chronic stroke survivors underestimate the motor ability of their affected upper limb. METHODS: 20 chronic stroke survivors (Mean FM: 28.2 ± 10.5) completed a 2D reaching task using an exoskeleton robot. Target sizes were individually altered to ensure success rates were similar for both upper limbs. Prior to each reaching movement, participants rated their confidence about successfully hitting the target (estimated upper limb motor ability). RESULTS: Confidence ratings were significantly lower for the affected upper limb (estimated ability), even though it was equally successful in the reaching task in comparison to the less affected upper limb (actual ability). Furthermore, confidence ratings did not correlate with level of impairment. CONCLUSIONS: Our results demonstrate that chronic stroke survivors can underestimate the actual motor abilities of their affected upper limb, independent of impairment level. Low confidence in affected upper limb motor abilities should be considered as a therapeutic target to increase the incorporation of the affected upper limb into activities of daily living.

Meta-análisis: Intervalos de confianza e Intervalos de predicción / Meta-analysis: Confidence intervals and Prediction intervals

En los informes meta-analíticos se suelen reportar varios tipos de intervalos, hecho que ha generado cierta confusión a la hora de interpretarlos. Los intervalos de confianza reflejan la incertidumbre relacionada con un número, el tamaño del efecto medio paramétrico. Los intervalos de predicción reflejan el tamaño paramétrico probable en cualquier estudio de la misma clase que los incluidos en un meta-análisis. Su interpretación y aplicaciones son diferentes. En este artículo explicamos su diferente naturaleza y cómo se pueden utilizar para responder preguntas específicas. Se incluyen ejemplos numéricos, así como su cálculo con el paquete metafor en R.(AU)

Several types of intervals are usually employed in meta-analysis, a fact that has generated some confusion when interpreting them. Confidence intervals reflect the uncertainty related to a single number, the parametric mean effect size. Prediction intervals reflect the probable parametric effect size in any study of the same class as those included in a meta-analysis. Its interpretation and applications are different. In this article we explain in de-tail their different nature and how they can be used to answer specific ques-tions. Numerical examples are included, as well as their computation with the metafor Rpackage.(AU)

Association between oral lichen planus and Candida albicans infection: a systematic review and meta-analysis.

This study examines the potential association between Oral Lichen Planus (OLP) and Candida albicans infection, exploring its potential impact on the development of OLP. A meta-analysis of individual case-control studies was performed, estimating odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). A quality assessment of the literature was conducted using the Newcastle-Ottawa Scale (NOS). Due to considerable heterogeneity in the selected studies, subgroup analyses were performed based on geographical location and recruitment methods. No significant publication bias was detected. A sensitivity analysis validated the robustness of the findings when applying a random-effects model. The meta-analysis included ten studies, comprising 1,124 OLP patients and 1,063 healthy controls. Results indicated a significantly higher detection rate of Candida albicans in OLP patients compared to healthy controls (OR = 1.74, P = 0.003, 95% CI: 1.20, 2.52). Additionally, an increased risk of Candida albicans infection was observed in erosive OLP (E-OLP) patients compared to healthy controls (OR = 3.97, 95% CI: 2.31, 6.84, P < 0.00001). These findings suggest a complex interplay between OLP and Candida albicans, highlighting the need for further research to elucidate the varying susceptibilities among different clinical types of OLP. This study provides novel insights for future research directions and clinical treatment strategies in this field.

Common odds ratio test and interval estimation for stratified bilateral and unilateral data.

In clinical research, data are commonly collected bilaterally from paired organs or bodily parts within individual subjects. However, unilateral data arise when constraints or limiting factors impede the collection of complete bilateral data. In this article, we propose three large-sample tests and five confidence interval methods for making inferences on the common treatment effect, measured by the odds ratio, in a stratified design under integrated bilateral and unilateral data. Our simulation results show that the likelihood ratio-based and score-based tests, along with their associated confidence interval methods, demonstrate robust control of type I error and close-to-nominal coverage probabilities. We apply the proposed methods to real-world datasets of acute otitis media and myopic eyes to showcase their validity and applicability in clinical practice.

A Comparison of Green, Delta, and Monte Carlo Methods to Select an Optimal Approach for Calculating the 95% Confidence Interval of the Population-attributable Fraction: Guidance for Epidemiological Research.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator [V(ß Ì‚)]) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the "GDM-PAF CI Explorer," was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland's method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, V[ß Ì‚] was identified as the most influential parameter in the estimation of CIs. Conclusions: This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Educational preparedness and perceived importance on confidence in new graduate registered nurses' medication administration.

BACKGROUND: Medication errors are the most common type of error affecting patient safety and the most preventable cause of adverse medical events globally. Medication errors occur most frequently (33.3 %) during the administration phase. New nurses felt their education left them vulnerable to errors, suggesting that current curricula may be insufficient. PURPOSE: The purpose of this study was to determine the relationship between new nurses' educational preparedness and perceived importance with confidence in medication administration. A secondary aim was to determine the difference in the variables based on demographic information. METHODS: A descriptive, correlational design was employed using the Theory of Human Error. Ohio newly licensed nurses were surveyed for their educational preparedness, perceived importance, and confidence in nine medication competencies. Descriptive and inferential statistics were used. RESULTS: N = 201. A significant, positive relationship was found between both educational preparedness and confidence, and perceived importance and confidence. Nurses reported high levels of educational preparedness, perceived importance, and confidence. All correlations and regressions were significant, indicating that as nurse educational preparedness and/or perceived importance increases, the odds of confidence increases. Demographic analysis demonstrated that practice area and years of experience significantly contributed to differences in the variables. CONCLUSIONS: Understanding the educational preparedness and perceived importance of the medication competencies can help guide future research into creating educational and clinical interventions to ultimately decrease medication errors.

Introducing an Emergency Department Electronic Handbook to Junior Doctors New to Emergency Medicine.

Background The transition of junior doctors into working in the emergency department (ED) in the United Kingdom often poses challenges in adapting to new hospital systems and protocols. To address this issue at Queen Elizabeth Hospital, King's Lynn (QEHKL), a quality improvement project (QIP) was undertaken to develop an electronic ED handbook with the primary aim of enhancing the confidence and knowledge of newly appointed doctors during their ED rotation. This electronic handbook serves as a comprehensive repository for vital medical protocols, guidelines, and trust referral pathways, offering an easily accessible resource for junior doctors. Objectives The primary objective of this study was to determine whether there was an improvement in the confidence and knowledge of ED junior doctors following the introduction of the Electronic ED Handbook. The secondary objectives were to determine whether introducing the ED Handbook increased the overall satisfaction rating of the content of the ED Junior Doctor Induction program and assess the level of recommendation for the ED Handbook among the doctors for inclusion in future ED inductions. Method The QIP was designed using the Model for Improvement framework, Plan, Do, Study, Act (PSDA). The aims were designed to be Specific, Measurable, Achievable, Relevant, and Time-bound (SMART). Pre- and post-intervention surveys were conducted for comparison before and after the ED Handbook was introduced. Results Regarding the confidence of junior doctors to proceed into their new roles, the responses of "quite confident" or "very confident" increased from 77.8% (before) to 100% (after the ED Handbook introduction). One hundred percent of the responders found the ED Handbook to be either "very useful" or "extremely useful" in increasing their confidence and knowledge in the first month of their ED rotation. The satisfaction rating of "excellent" for the content of the ED Junior Doctor Induction program increased from 55.5% to 66.7%. One hundred percent of the responders recommended the inclusion of the ED Handbook for future inductions. Conclusion and recommendations Comparing the results from the pre- and post-intervention surveys shows a significant improvement in the confidence and knowledge of ED junior doctors following the introduction of the Electronic ED Handbook. The handbook was formally endorsed by the ED clinical governance team as an integral component of the ED induction process, aiding junior doctors in making a seamless transition into their new roles in emergency medicine. This study emphasizes the importance of utilizing digital resources to improve the confidence and knowledge of junior doctors and recommends the continued use of the handbook in future induction programs.

Confidence and barriers: Analysis of factors associated with timely routine childhood vaccination in Canada during the COVID-19 pandemic.

Routine childhood vaccination is a crucial component of public health in Canada and worldwide. To facilitate catch-up from the global decline in routine vaccination caused by the COVID-19 pandemic, and toward the ongoing pursuit of coverage goals, vaccination programs must understand barriers to vaccine access imposed or exacerbated by the pandemic. We conducted a regionally representative online survey in January 2023 including 2036 Canadian parents with children under the age of 18. We used the COM-B model of behaviour to examine factors influencing vaccination timeliness during the pandemic. We assessed Capability with measures of vaccine understanding and decision difficulty, and Motivation with a measure of vaccine confidence. Opportunity was assessed through parents' self-reported experience with barriers to vaccination. Twenty-four percent of surveyed parents reported having missed or delayed one of their children's scheduled routine vaccinations since the beginning of the pandemic, though most parents reported having either caught up or the intention to catch up soon. In the absence of opportunity barriers, motivation was associated with timely vaccination for children aged 0-4 years (aOR = 1.81, 95 % CI: 1.14-2.84). However, experience with one or more opportunity barriers, particularly clinic closures and difficulties getting an appointment, eliminated this relationship, suggesting perennial and new pandemic-associated barriers are a critical challenge to vaccine coverage goals in Canada.

Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions.

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

PEZy-miner: An artificial intelligence driven approach for the discovery of plastic-degrading enzyme candidates.

Plastic waste has caused a global environmental crisis. Biocatalytic depolymerization mediated by enzymes has emerged as an efficient and sustainable alternative for plastic treatment and recycling. However, it is challenging and time-consuming to discover novel plastic-degrading enzymes using conventional cultivation-based or omics methods. There is a growing interest in developing effective computational methods to identify new enzymes with desirable plastic degradation functionalities by exploring the ever-increasing databases of protein sequences. In this study, we designed an innovative machine learning-based framework, named PEZy-Miner, to mine for enzymes with high potential in degrading plastics of interest. Two datasets integrating information from experimentally verified enzymes and homologs with unknown plastic-degrading activity were created respectively, covering eleven types of plastic substrates. Protein language models and binary classification models were developed to predict enzymatic degradation of plastics along with confidence and uncertainty estimation. PEZy-Miner exhibited high prediction accuracy and stability when validated on experimentally verified enzymes. Furthermore, by masking the experimentally verified enzymes and blending them into homolog dataset, PEZy-Miner effectively concentrated the experimentally verified entries by 14∼30 times while shortlisting promising plastic-degrading enzyme candidates. We applied PEZy-Miner to 0.1 million putative sequences, out of which 27 new sequences were identified with high confidence. This study provided a new computational tool for mining and recommending promising new plastic-degrading enzymes.

A Correlational Study on Professional Identity and Self-Efficacy Among Nursing Students.

Aim To conduct a correlational study on professional identity and self-efficacy among nursing students Background Professional identity, in simple terms, refers to how one perceives oneself in relation to one's profession. Self-efficacy is defined as people's self-confidence in facing challenges and breaking through difficulties. A well-developed level of self-efficacy may enhance professional identity. This study sought to assess the professional identity and self-efficacy of student nurses enrolled at SRM College of Nursing, SRM Institute of Science and Technology, Kattankulathur, India, examine the relationship between professional identity and self-efficacy, and explore how self-efficacy and professional identity levels relate to demographic variables. Methodology A descriptive research design was utilized to assess the professional identity as well as the self-efficacy of 202 student nurses. The subjects were surveyed using the General Self-Efficacy Scale and Professional Identity Scale for Nursing Students questionnaires to analyze their levels of self-efficacy and professional identity, respectively. Result The results indicate that among the 202 students surveyed, 102 (50.5%) possess a moderate level of self-efficacy, and 71 (35.1%) possess a moderate level of professional identity. A strong positive correlation was found between professional identity and self-efficacy (r=0.489), implying that the student nurses with a prominent degree of self-efficacy have a compelling degree of professional identity and vice-versa. Conclusion In this investigation, most of the students demonstrated moderate levels of self-efficacy as well as professional identity. Additionally, a robust correlation was observed between self-efficacy and professional identity.

Connect, collaborate and tailor: a model of community engagement through infographic design during the COVID-19 pandemic.

BACKGROUND: Across the globe, racial and ethnic minorities have been disproportionately affected by COVID-19 with increased risk of infection and burden from disease. Vaccine hesitancy has contributed to variation in vaccine uptake and compromised population-based vaccination programs in many countries. Connect, Collaborate and Tailor (CCT) is a Public Health Agency of Canada funded project to make new connections between public health, healthcare professionals and underserved communities in order to create culturally adapted communication about COVID-19 vaccines. This paper describes the CCT process and outcomes as a community engagement model that identified information gaps and created tailored tools to address misinformation and improve vaccine acceptance. METHODS: Semi-structured interviews with CCT participants were undertaken to evaluate the effectiveness of CCT in identifying and addressing topics of concern to underserved and ethnic minority communities. Interviews also explored CCT participants' experiences of collaboration through the development of new partnerships between ethnic minority communities, public health and academic researchers, and the evolution of co-operation sharing ideas and creating infographics. Thematic analysis was used to produce representative themes. The activities described were aligned with the levels of public engagement described in the IAP2 spectrum (International Association for Public Participation). RESULTS: Analysis of interviews (n = 14) revealed that shared purpose and urgency in responding to the COVID-19 pandemic motivated co-operation among CCT participants. Acknowledgement of past harm, present health, and impact of social inequities on public service access was an essential first step in establishing trust. Creating safe spaces for open dialogue led to successful, iterative cycles of consultation and feedback between participants; a process that not only helped create tailored infographics but also deepened engagement and collaboration. Over time, the infographic material development was increasingly directed by community representatives' commentary on their groups' real-time needs and communication preferences. This feedback noticeably guided the choice, style, and presentation of infographic content while also directing dissemination strategies and vaccine confidence building activities. CONCLUSIONS: The CCT process to create COVID-19 vaccine communication materials led to evolving co-operation between groups who had not routinely worked together before; strong community engagement was a key driver of change. Ensuring a respectful environment for open dialogue and visibly using feedback to create information products provided a foundation for building relationships. Finally, our data indicate participants sought reinforcement of close cooperative ties and continued investment in shared responsibility for community partnership-based public health.

Effects of gamification on academic motivation and confidence of undergraduate nursing students: A systematic review and meta-analysis.

OBJECTIVES: This review aimed to evaluate the effects of gamification on academic motivation and confidence among undergraduate nursing students and identify the game design elements contributing to these effects. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Comprehensive systematic searches were conducted to retrieve randomized controlled trials (RCTs) and quasi-experimental studies (QES) with control groups published in English and Korean from inception to January 31, 2024, using PubMed, Embase, CINAHL Plus, ERIC, ProQuest Central, Cochrane Library, and RISS. REVIEW METHODS: Eligible studies, including grey literature, were selected. The quality of the selected studies was evaluated using the Joanna Briggs Institute Critical Tool. Meta-analyses based on a random-effects model were conducted to estimate the standardized pooled effects (SMD). Subgroup analyses were conducted to identify the effect size moderators and game design elements that contributed to the effect size. The grading of recommendations, assessment, development, and evaluation approach (GRADE) was used to evaluate the certainty of evidence. RESULTS: A total of 22 studies were selected for the systematic review, and 18 studies were included in the meta-analysis. The SMD for academic motivation (SMD of RCTs = 0.86, 95 % CI [0.27, 1.45]; SMD of QES = 1.22, 95 % CI [0.17, 2.26]) and confidence (SMD of RCTs = 1.11, 95 % CI [0.54, 1.68]; SMD of QES = 0.79, 95 % CI [0.40, 1.19]) revealed moderate-to-large effects. The subgroup analysis revealed significant differences in effect sizes across academic years, measurement scales, study areas, study quality, game duration, and game design elements. GRADE assessments for academic motivation and confidence were rated as moderate and low, respectively. CONCLUSION: This review provides convincing evidence for the positive effects of gamification interventions on academic motivation and confidence among undergraduate nursing students. However, the limited number of RCTs and moderate-to-low certainty of the evidence underscore the need for additional research.

Model-agnostic confidence measurement for aggregating multimodal ensemble models in automatic diagnostic systems.

Automatic diagnostic systems (ADSs) have been garnering increased attention because they can alleviate the workload of clinicians by assisting in diagnosis and offering low-cost access to healthcare for people in medically underserved areas. ADS can suggest potential diseases by analyzing a patient's self-report. Previous research on ADS has leveraged diagnostic case data from various patients and medical knowledge to diagnose diseases, with multimodal ensemble methods proving particularly effective. However, the existing multimodal ensemble method combines the probabilities of different models in the aggregating process, which can not properly combine the probabilities that are produced by different criteria. To address these issues, we propose an effective aggregation framework for multimodal ensembles that can properly aggregate model-agnostic confidence scores and predictions from each model. Our framework transforms probability scores from different criteria into unified aggregation rule-based scores and reflects the gap between the probabilities that may be blurred in the aggregation process through the confidence score. In particular, The proposed confidence measurement method employs a post-analysis approach with the developed model or algorithm, making it adaptable in a model-agnostic manner and suitable for multimodal ensemble learning that utilizes heterogeneous prediction results. Our experimental results demonstrate that our framework outperforms existing approaches by more effectively leveraging the strengths of each ensemble member.

Post-selection inference in regression models for group testing data.

We develop a methodology for valid inference after variable selection in logistic regression when the responses are partially observed, that is, when one observes a set of error-prone testing outcomes instead of the true values of the responses. Aiming at selecting important covariates while accounting for missing information in the response data, we apply the expectation-maximization algorithm to compute maximum likelihood estimators subject to LASSO penalization. Subsequent to variable selection, we make inferences on the selected covariate effects by extending post-selection inference methodology based on the polyhedral lemma. Empirical evidence from our extensive simulation study suggests that our post-selection inference results are more reliable than those from naive inference methods that use the same data to perform variable selection and inference without adjusting for variable selection.

Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy.

PURPOSE: We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third-party) AS models. METHODS: Thirty-two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel-wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with "difference to gold-standard" error maps. Nine cases were used for testing ACo performance on IAS and validation with two external deep learning segmentation model predictions [external model with low-quality AS (EM-LQ) and external model with high-quality AS (EM-HQ)]. Matthew's correlation coefficient (MCC), false-positive rate (FPR), false-negative rate (FNR), and visual assessment were used for evaluation. Edge removal and geometric distance corrections were applied to achieve more useful and clinically relevant confidence maps and performance metrics. RESULTS: ACo showed generally excellent performance on both internal and external segmentations, across all OARs (except lenses). MCC was higher on IAS and low-quality external segmentations (EM-LQ) than high-quality ones (EM-HQ). On IAS and EM-LQ, average MCC (excluding lenses) varied from 0.6 to 0.9, while average FPR and FNR were ≤0.13 and ≤0.21, respectively. For EM-HQ, average MCC varied from 0.4 to 0.8, while average FPR and FNR were ≤0.37 and ≤0.22, respectively. CONCLUSION: ACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference-free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic.

Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra-Class Correlation Coefficient in Inter-Rater Reliability Studies.

The agreement intra-class correlation coefficient (ICCa) is a suitable statistical index for inter-rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter-rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior-power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter-rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter-rater reliability studies.

Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET.

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (Cantorchilus leucotis) and Thrush-like Wren (Campylorhynchus turdinus). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species.

Practice and confidence in electrocardiogram interpretation among ICU nurses: A cross-sectional study.

OBJECTIVES: This study aimed to determine practice and confidence in electrocardiogram (ECG) interpretation among intensive care unit (ICU) nurses in Fujian Province, China, and identify predictors of ECG interpretation practice. RESEARCH METHODOLOGY/DESIGN: A quantitative cross-sectional study was conducted between October 2021 and December 2021 among 357 respondents. SETTING: Conducted online at twenty-one hospitals in all nine cities of Fujian Province. MAIN OUTCOME MEASURES: Purposive and convenient sampling techniques were employed in selecting hospitals and respondents, respectively. A validated and pre-tested Chinese version of the questionnaire was used in data collection. We conducted binary logistic regression to identify the predictors of ICU nurses' ECG interpretation practice, and linear regression to analyze the relationship between ECG interpretation practice and confidence. We considered statistically significant a p-value < 0.05. RESULTS: The practice mean score of the respondents was 5.54 (SD = 2.26) out of 10 points, and only 2.2 % of nurses correctly interpreted all the patient ECG strips. Few ICU nurses (25.5 %) had good ECG interpretation practice, with a confidence mean score of 2.02 (SD = 0.99) out of 4 points in their overall ability to interpret patient ECG strips. Currently working unit in comparison to cardiac ICU (emergency ICU: AOR = 5.71, 95 % CI: 1.84-17.75); previous ECG training (AOR = 2.02, 95 % CI: 1.10-3.70); source of ECG training (university/school) (AOR = 2.02, 95 % CI: 1.12-3.65); and ECG knowledge (AOR = 16.18, 95 % CI: 7.43-35.25) were significantly associated with the ECG interpretation practice. CONCLUSIONS: ICU nurses' ECG interpretation practice in the current study was relatively poor. An ECG education program is recommended to impart ICU nurses with basic ECG knowledge for enhancing good ECG interpretation practice and confidence in nursing care provision. IMPLICATIONS FOR CLINICAL PRACTICE: Good ECG interpretation skills are paramount among ICU nurses for better patient outcomes. ECG knowledge among ICU nurses is an important predictor of effective ECG monitoring for cardiac arrhythmias. Therefore, frequent, continuouszgood practice and boost confidence in the provision of quality nursing care.

A narrative review of factors affecting memory confidence in the context of compulsive checking: A search for evidence-based potential therapeutic targets to improve memory confidence.

BACKGROUND AND OBJECTIVES: According to current models of compulsive checking, memory confidence greatly contributes to the development and maintenance of checking behaviours. However, how to intervene in memory confidence in an evidence-based manner has not yet been fully understood. Thus, the purpose of the current paper was to identify the factors influencing memory confidence through the review of experimental evidence. METHODS: PubMed, Google Scholar, OpenGrey and ProQuest databases were searched by combining two sets of keywords related to memory confidence and checking. Our search yielded 24 experiments. Due to the considerable heterogeneity of the studies regarding questionnaires, tasks and paradigms used, data were synthesized using a narrative review approach. RESULTS: Six factors emerged from a thorough review of the literature, including negative memory belief, higher memory standard, inflated sense of responsibility, familiarization with the checked stimuli, number of checks and anxious valence of the checked stimuli. CONCLUSION: The findings have important implications for the treatment of compulsive checking. We suggested general guidelines to translate these factors into a novel intervention to increase memory confidence in compulsive checkers.