Behavioral Indicators Research Articles

From: “It’s just how she walks …” to “… any lameness is a welfare issue” – UK stakeholders’ perspectives on chronic lameness in dairy cows

ABSTRACT Lameness in dairy cows is a prevalent welfare problem and imposes direct and indirect economic costs on producers. Lameness can be acute or chronic, however, a shared definition of what constitutes chronic lameness is currently lacking amongst stakeholders. Our study used structured interviews to address this gap. We interviewed 22 UK-based participants (eight veterinarians, eight dairy farmers, three foot-trimmers; two were veterinarians and dairy farmers; another was a farmer and foot trimmer). Stakeholder responses found good agreement with work done by previous authors, e.g., on behavioral and production-related lameness indicators; and that lame cows experience pain because of their lameness and, thus, suffer compromised welfare. Participants used descriptive terms (e.g., “recurrent”) and suggested measurable characteristics (e.g., bout duration) to define chronic lameness. Stakeholders reported that chronic lameness not only affects cow welfare but also reduces the wellbeing of people working with them; and also raised concerns over the environmental costs of chronic lameness, such as increased culling and reduced sustainability. Developing some of our findings into a future definition of chronic lameness would be the first step in understanding and quantifying the extent of this problem.

App-Based Ecological Momentary Assessment of Problematic Smartphone Use During Examination Weeks in University Students: 6-Week Observational Study.

The increasing prevalence of problematic smartphone use (PSU) among university students is raising concerns, particularly as excessive smartphone engagement is linked to negative outcomes such as mental health issues, academic underperformance, and sleep disruption. Despite the severity of PSU, its association with behaviors such as physical activity, mobility, and sociability has received limited research attention. Ecological momentary assessment (EMA), including passive data collection through digital phenotyping indicators, offers an objective approach to explore these behavioral patterns. This study aimed to examine associations between self-reported psychosocial measures; app-based EMA data, including daily behavioral indicators from GPS location tracking; and PSU in university students during the examination period. A 6-week observational study involving 243 university students was conducted using app-based EMA on personal smartphones to collect data on daily behaviors and psychosocial factors related to smartphone overuse. PSU was assessed using the Korean Smartphone Addiction Proneness Scale. Data collected from the Big4+ app, including self-reports on mood, sleep, and appetite, as well as passive sensor data (GPS location, acceleration, and steps) were used to evaluate overall health. Logistic regression analysis was conducted to identify factors that significantly influenced smartphone overuse, providing insights into daily behavior and mental health patterns. In total, 23% (56/243) of the students exhibited PSU. The regression analysis revealed significant positive associations between PSU and several factors, including depression (Patient Health Questionnaire-9; odds ratio [OR] 8.48, 95% CI 1.95-36.87; P=.004), social interaction anxiety (Social Interaction Anxiety Scale; OR 4.40, 95% CI 1.59-12.15; P=.004), sleep disturbances (General Sleep Disturbance Scale; OR 3.44, 95% CI 1.15-10.30; P=.03), and longer sleep duration (OR 3.11, 95% CI 1.14-8.48; P=.03). Conversely, a significant negative association was found between PSU and time spent at home (OR 0.35, 95% CI 0.13-0.94; P=.04). This study suggests that negative self-perceptions of mood and sleep, along with patterns of increased mobility identified through GPS data, increase the risk of PSU, particularly during periods of academic stress. Combining psychosocial assessments with EMA data offers valuable insights for managing PSU during high-stress periods, such as examinations, and provides new directions for future research.

Behavioural Indicators of Usability in Visual Analytics Dashboards

Information presentation problems on interactive dashboards are known to hinder decision-making. Since a traditional user-centred approach to designing usable dashboards cannot fully satisfy user demands, needs and skills, we isolate behavioural indicators of usability when users conduct typical information-seeking and comparison tasks. In a first study (N=50), we identified strategies derived from 486,435 interaction events logged in a controlled setting with synthetic dashboards. User models consisting of these user strategies and graph literacy produced strong signals indicating that usability was predictable. In a second study (N=65), we tested the initial insights on real-world dashboards. While most of our hypotheses were confirmed, graph literacy emerged as the best predictor of usability. Usability was better predicted in dashboards with problems, suggesting promising opportunities for automated usability evaluation and real-time support for users struggling with visual analytics dashboards.

Capturing changes in social functioning and positive affect using ecological momentary assessment during a 12-month trial of xanomeline and trospium chloride in schizophrenia.

Experiential negative symptoms (NS) are determinants of disability in schizophrenia (SCZ). Xanomeline/Trospium Chloride (X/T), an M1/M4 muscarinic receptor agonist, is an approved monotherapy for treatment of schizophrenia, including NS. We used remote ecological momentary assessments (EMA) to track changes in indicators of NS during 12months of outpatient treatment with X/T. After discontinuing previous medications, 566 outpatients with SCZ received open-label X/T monotherapy for up to 12months. Participants completed 3 EMA surveys 7days a week, one week a month. Surveys queried whether participants were home vs. away and alone vs with someone, as well as productive and unproductive activities, and moods. Hierarchical linear modeling (HLM) examined temporal changes and the relationships between the indicators of NS. 500 participants answered one or more EMA surveys and 350 met 33% adherence criteria, answering a total of 40,464 surveys, with overall adherence among these participants at 66%. During treatment with X/T, there were significant decreases in surveys at home (p<.001), alone (p<.001), and engaging in unproductive activities (p<.001). There were significant increases in productive activities both home (p<.001) and away (p<.001) and in positive affect (PA) (p<.001). Improvements in PA converged with reduced unproductive activities, particularly when others were present (p<.001). Behavioral indicators of NS improved early and were sustained with X/T treatment. Improvements were multidimensional, shifting toward more time with others, away from home, and engaged in productive activities. These improvements were associated with increases in PA, consistent with previous EMA studies of NS.

A Qualitatively-Informed Approach to Examining the Construct Validity of Alcohol-Involved Sexual Violence

Abstract One in five women experience sexual victimization in her lifetime, making sexual victimization a significant public health crisis. This public health crisis also has detrimental health consequences. Understanding the scope of sexual violence and the effects of prevention programming requires accuracy in measuring sexual violence. Yet, a wealth of research suggests lack of precision in sexual violence measurement and potential confusion among respondents in how to interpret items on sexual victimization and perpetration questionnaires. Specifically, alcohol-involved sexual violence questions are among the hardest to operationalize in such questionnaires, due, in part to cultural ideas about alcohol and idiosyncratic effects of alcohol on behavior. The current study addressed this gap by assessing the construct validity of alcohol-involved sexual violence questionnaire items. We used a cognitive-interviewing qualitative approach to analyze interviews with forty participants to better understand the cognitive and interpretive language nuances between “took advantage of while drunk” and being “too drunk to consent.” Qualitative thematic analysis was conducted to identify and summarize differences in how stimuli were perceived and interpreted. Our thematic analysis identified three themes: (a) respondents used behavioral indicators as an indicator of impaired decision-making, (b) respondents perceived and expressed complexity in understanding alcohol-related consent, and (c) some respondents attributed the differences between the phrases to reflect experiences of either perpetration and victimization. The results underscore the importance of refining questionnaires using behavioral indicators of alcohol intoxication to differentiate between incapacitated and impaired alcohol-involved incidents, clarifying non-consent language, and being purposeful in describing a state or an action related to the consumption of alcohol, to better measure alcohol-involved sexual violence.

Advanced Machine Learning Techniques for Classification and Detection of Network Intrusions in Cyber-Physical Systems

IDS keep an eye out for any indications of malicious behaviour or policy breaches in a network or system. An administrator should be notified or a central record should be kept of any intrusion activity or violation using a security information and event management system. Through the integration of sensing, computation, control, and networking, cyber physical systems connect physical infrastructure and objects to the internet and to each other. Therefore, it is necessary to have sufficient intrusion detection after safeguard the CPS from cyber-attacks, which might corrupt the equipment. CPS network intrusion categorisation and detection using sophisticated ML algorithms is the subject of this study's thorough methodology. Some of the methodical stages of the study were data collection preprocessing, and feature selection of the internet traffic that were used in the work which used NSL-KDD dataset for intrusion detection. As a result, a range of ML models is employed in classification applications that exploit their main benefits, including RF, LR, and XGBoost. This model's performance is evaluated using metrics that are based on recall, accuracy, precision, and F1 score. Confusion matrix visualisations are also included for efficient comprehension of classification results. The findings reveal that RF achieved a highest accuracy at 99.50%, followed closely by XGBoost at 99.41%, while LR recorded an accuracy of 95.39%. In addition, Kappa coefficient test, precision99.65 %, recall99.38%, and F1 score99.51 % in RF were observed. In total, the results reveal Random Forest as the superior model in comparison to employed LR and XGBoost while creating accurate IDS to protect CPS sufficiently.

Effects of administering local anesthesia immediately before surgical castration on indicators of pain and discomfort of beef calves

Abstract Forty Hereford cross calves (mean and SD of 47.9 ± 10.43 d old) were used to assess the efficacy of lidocaine administered immediately before surgical castration on physiological and behavioral indicators of pain and discomfort. Calves were assigned by age to one of two treatments: surgical castration following subcutaneous injection of 2.5 mL/100 kg body weight of meloxicam (Metacam 20 mg/mL, Boehringer Ingelhein, Burlington, ON, Canada) per kg body weight (MEX, n=19); or the same treatment preceded 90 s before by a nerve block of the spermatic cord using 8 mL of buffered (1 mL:10 mL of 8.4% sodium bicarbonate USP, Hospira Inc., Lake Forest, IL, USA) lidocaine (4 mL per cord; 20 mg/mL, LIDO-2 with epinephrine, Rafter Products, Calgary, AB, Canada) (LID, n=21). During the castration procedure, two observers scored how much pain each animal was experiencing (visual analog score, VAS), number of body shakes and leg kicks for each calf. Blood samples and exit scores were collected the day of castration and on d 7, and 14 to assess the neutrophil-to-lymphocyte ratio (N:L) and calf temperament, respectively. Hair samples were collected the day of castration and on d 14 to determine cortisol levels. Scrotal circumference, swelling, and healing scores were determined on d 7 and 14 to assess healing. An accelerometer (HOBO Pendant® G Data Logger, Onset, Cape Cod, MA) was placed on the left hind leg to measure lying behavior until d 6 after castration. Calves were video recorded for one hour after the castration procedure to observe behaviors indicative of pain and discomfort. Surveillance cameras were also used to assess the mobility of the calves when cow-calf pairs were moved from the holding pen to the pasture. The VAS, number of tail flicks and foot stamps were greater (P &amp;lt; 0.01) in MEX than in LID calves. During the first hour after castration, LID calves spent more time lying (P = 0.03) and less time standing (P = 0.01) than MEX calves. When moved from the holding pen, LID calves moved faster (P &amp;lt; 0.01) and closer to their dams (P &amp;lt; 0.05) than MEX calves. Hair cortisol was greater (P &amp;lt; 0.05) in LID than MEX calves on d 14. Results suggest that using lidocaine for local anesthesia immediately before surgical castration reduced behaviors indicative of pain and improved mobility post-castration.

Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study.

Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.

Longitudinal Analysis of the Transition Between Multimorbidity and Mortality Patterns from a Syndemic Perspective.

Longitudinal analysis of multimorbidity (MM) from a holistic approach to the aging population is necessary to guide medical care. To estimate the probability of transition between different MM patterns and the "death" stage and to characterize the socioeconomic, health, behavioral, and functional dimensions of MM patterns from a syndemic perspective. A cohort of 7,120 people ≥50 from the Mexican Health and Aging Study and mortality data at 11 and 17 years of follow-up were analyzed using the latent class approach. MM was defined as the coexistence of two or more chronic diseases (CDs). Socioeconomic, health, behavioral, and functional indicators were registered. Descriptive analysis, latent class analysis, and Markov chain models were carried out. The model included three latent classes: "healthy" (low probability of CD); "multi-organic" (high probability of hypertension, arthritis, diabetes mellitus, lung disease, and myocardial infarction); and "sensitive-emotional, mental, neurological" (high probability of chronic pain, depression, and cognitive impairment). At the first follow-up, people in the "multi-organic" class were more likely to move on to death, and at the second follow-up, they were in the "sensitive-emotional, mental, neurological" class. In addition, the characteristic profiles of the indicators for each latent class were identified. The probabilities of transition between the three classes of MM and death, as well as the socioeconomic, behavioral, and functional dimensions in those over 50 years of age, revealed, from a syndemic perspective, the variability in the likelihood of progressing toward an unfavorable outcome.

Caregiver report of infant behavior associated with autism likelihood in first year of life.

Identification of prodromal indicators of autism in infancy has the potential to identify behaviors relevant to early autism screening. We report on data from a prospective general population birth cohort with maternal reported measures at 9 and 12 months: the Survey of Well-Being of Young Children (SWYC; general developmental surveillance) and the First Year Inventory-Lite v3.1b (FYI-Lite; autism specific parent report research tool). Mothers completed the surveys and the Broad Autism Phenotype Questionnaire (BAPQ), a self-report measure of subclinical features of autism. In this sample of 332 infants (168 males), maternal-reported infant developmental milestones and behavioral indicators of difficult temperament, poor adaptability, and sleep problems at 9 months (SWYC) and maternal self-reported subclinical autism characteristics (BAPQ) were correlated with maternal-reported autism-related behaviors on the FYI-Lite at 12 months. Regression models revealed significant unique associations between infant temperament, developmental milestones, and FYI-Lite scores while controlling for significant effects of maternal BAPQ scores and education. Maternal report of infant temperament and developmental milestones at 9 months were associated with maternal-report early indicators of autism likelihood at 12 months in a general birth cohort. Follow up of this cohort is needed to determine associations with formal diagnostic outcomes. Identifying scalable measures of infant behaviors in general and specific to autism may help identify targets of intervention for infants in the first year of life. This study aims to contribute to improved first-year surveillance by assessing potential early autism indicators in a prospective general birth cohort, whereas other observational studies utilize enriched risk cohorts (e.g., infant siblings of autistic children). Preliminary findings of this cohort revealed that a maternal report of 9-month temperament significantly predicted higher scores on a maternal report 12-month autism screener and indicated the importance of considering maternal self-reported subclinical autism characteristics when interpreting parent report screeners.

Elliptic and Quadrangular Flow of Protons in the High-Baryon-Density Region

The collective flow provides valuable insights into the anisotropic expansion of particles produced in heavy-ion collisions and is sensitive to the equation of the state of nuclear matter in high-baryon-density regions. In this paper, we use the hadronic transport model SMASH to investigate the elliptic flow (v2), quadrangular flow (v4), and their ratio (v4/v22) in Au+Au collisions at high baryon density. Our results show that the inclusion of baryonic mean-field potential in the model successfully reproduces experimental data from the HADES experiment, indicating that baryonic interactions play an important role in shaping anisotropic flow. In addition to comparing the transverse momentum (pT), rapidity, and centrality dependence of v4/v22 between HADES data and model calculations, we also explore its time evolution and energy dependence across sNN= 2.4 to 4.5 GeV. While the ratio v4/v22 for high-pT particles approaches 0.5, which aligns with expectations from hydrodynamic behavior, we emphasize that this result primarily reflects agreement with the HADES measurements rather than a definitive indication of ideal fluid behavior. These findings contribute to understanding the early-stage dynamics in heavy-ion collisions at high baryon density.

Is “Rough Sex” a Thing? A Survey of Meaning

ABSTRACT The term “rough sex” is widely used, but it is not always clear what it means. Through exploring people’s working definitions of “rough sex,” we asked what they revealed about the underlying phenomenon it is applied to, and whether it is actually a thing. Our online survey of 567 New Zealand respondents (73.7% women, 21% men, 5.3% gender diverse; mean age, 35.6 years, SD = 10.8) identified various behaviors that many considered to be part of “rough sex” – including hair pulling, holding down someone forcefully, slapping and “choking.” While this behavioral profile was similar to previous U.S. studies, our survey was designed with novel features that allowed us to look more closely at the variation in how people make sense of “rough sex.” Embedded within a critical psychology approach, our descriptive analysis highlighted areas of ambiguity and difference, including points of contradiction across people’s working definitions. Critical qualitative analysis of open-ended textual data further examined categorical misalignments and the difficulties in interpreting behavioral indicators as defining of “rough sex” – especially without more contextual information. Overall, we found considerable variability and some contradictions in people’s definitions of “rough sex” and the meanings they ascribed to it. Our findings led us to argue that the term “rough sex” does not map onto a coherent phenomenon, and that use of the term can be misleading, with potentially problematic implications.

Age-Related Changes in Gut Health and Behavioral Biomarkers in a Beagle Dog Population.

The gut and the gut microbiome communicate with the nervous system through the gut-brain axis via neuroimmune and neuroendocrine mechanisms. Despite existing research, studies exploring this link in aging dogs are limited. This study aims to examine multiple blood and fecal biomarkers of intestinal health, along with various behavioral indicators based on saliva, blood, observations, and activity, in different age populations (junior: <2 y.o.; adult: 2-7 y.o.; senior: >7 y.o.) of thirty-seven Beagle dogs. In our study, Bacteroides were significantly higher in senior dogs. The relative abundance of Faecalibacterium and Blautia showed age-related trends, higher in senior and junior dogs, respectively. Fecal short-chain fatty acid concentration, especially acetate, increased with age, while propionate was higher in junior dogs. For the behavioral indicators we considered, blood thyroxine concentration, playing, exploring, and total activity were higher in junior dogs. The differences observed between the biomarkers of gut health and behavior, particularly those significant for the age correlations, emphasize the importance of considering age-related factors when studying the gut microbiome and behavior. However, further research is needed to better understand the mechanisms and specific pathways involved in the relationship between the studied biomarkers and age.

Machine learning approach to student performance prediction of online learning.

Student performance is crucial for addressing learning process problems and is also an important factor in measuring learning outcomes. The ability to improve educational systems using data knowledge has driven the development of the field of educational data mining research. Here, this paper proposes a machine learning method for the prediction of student performance based on online learning. The critical thought is that eleven learning behavioral indicators are constructed according to online learning process, following that, through analyzing the correlation between the eleven learning behavioral indicators and the scores obtained by students online learning, we filter out those learning behavioral indicators that are weakly correlated with student scores, meanwhile, retain these learning behavior indicators being strongly correlated with student scores, which are used as the eigenvalue indicators. Finally, using the eigenvalue indicators to train the proposed logistic regress model with Taylor expansion. Experimental results show that the proposed logistic regress model defeats against the comparative models in prediction ability. Results also indicate that there is a significant dependency between students' initiative in learning and learning duration, nevertheless, learning duration has a significant effect on the prediction of student performance.

Real-Time Driver Drowsiness Detection System Using Machine Learning

Driver fatigue is a leading cause of road accidents globally, with significant fatalities and injuries reported annually. This paper presents a real-time Driver Drowsiness Detection System leveraging computer vision and machine learning techniques. Using Convolutional Neural Networks (CNNs), the system monitors key behavioural indicators, such as eye closure and yawning frequency, from live video feeds. Preprocessing techniques like grayscale conversion and normalization ensure robustness under varied conditions. Alerts are triggered immediately upon detecting drowsiness, enhancing road safety. The system’s non-intrusive design ensures scalability and affordability, making it suitable for integration into Advanced Driver Assistance Systems (ADAS). This paper also discusses challenges and potential enhancements, such as IoT integration and edge computing. Key Words: Driver Drowsiness Detection, Machine Learning, CNNs, Real-Time Monitoring, Road Safety.

The Impact of Acute Aerobic Exercise on General and Food-Related Inhibitory Function Among Young Adults with Obesity: An Event-Related Potential (ERP) Study.

Backgrounds and Objectives: Obesity presents a significant global public health challenge and is associated with declines in both general and food-related inhibitory control, crucial for maintaining a healthy weight and preventing obesity progression. An increasing body of research suggests that acute aerobic exercise may improve inhibitory function. However, the effects and underlying mechanisms of acute aerobic exercise on both general and food-related inhibition in obese adults remain unclear. This study aimed to explore the potential impacts and underlying neuroelectronic mechanisms of a single session of aerobic exercise at varying intensities on general and food-related inhibitory functions among young adult males with obesity. Design: A within-subject design comprising three sessions (control, low-intensity exercise, moderate-intensity exercise) × three picture types (high-calorie food, low-calorie food, neutral picture) was employed. Methods: Eighteen young adult males with obesity [body mass index (BMI): 34.60 ± 4.21 kg/m2, aged 24.50 ± 5.13 years (Mean ± SD)] were recruited. They participated in three intervention sessions: acute aerobic exercise at low [40-50% maximal Heart Rate (HRmax)], moderate (65-70% HRmax), and a control session (sitting rest), separated by five-day intervals in a counterbalanced order. Following each session, participants performed a food-related Go/No-go task, and EEG recordings (N2 and P3 components) were conducted within 15 min. Results: Moderate-intensity exercise elicited larger N2 amplitudes compared to the control session across different picture types and task conditions. However, there was no significant effect on behavioral indicators or P3 amplitude across sessions. Additionally, food stimuli (both high- and low-calorie) resulted in lower No-go accuracy and smaller N2 amplitudes compared to neutral stimuli. Conclusions: Acute moderate-intensity exercise might influence general and food-related inhibitory function in obese individuals at the neuroelectric stage, potentially by enhancing attentional resources for managing cognitive control and conflict detection. Moreover, reduced N2 amplitudes and No-go accuracy in response to food stimuli compared to non-food stimuli indicate a diminished ability to allocate attentional and neural resources to manage food-related conflicts. However, due to the relatively small sample size, caution is advised when generalizing these findings to the broader population. The pilot test indicated that obese participants had difficulty sustaining high-intensity exercise at 80-90% of their maximum heart rate for a continuous or 20 min period, highlighting potential challenges in exercise adherence at high intensities within this population. Future research is needed to utilize interdisciplinary approaches and multimodal technologies to clarify how exercise influences food-related cognition, appetite regulation, and brain mechanisms in obesity, aiming to better contribute to the prevention and treatment of obesity.

Alcohol and emotion: Analyzing convergence between facially expressed and self-reported indices of emotion under alcohol intoxication.

Emotion measurement is central to capturing acute alcohol reinforcement and so to informing models of alcohol use disorder etiology. Yet our understanding of how alcohol impacts emotion as assessed across diverse response modalities remains incomplete. The present study leverages a social alcohol-administration paradigm to assess drinking-related emotions, aiming to elucidate impacts of intoxication on self-reported versus behaviorally expressed emotion. Participants (N = 60; Mage = 22.5; 50% male; 55% White) attended two counterbalanced laboratory sessions, on one of which they were administered an alcoholic beverage (target blood alcohol content .08%) and on the other a nonalcoholic control beverage. Participants in both conditions were accurately informed of beverage contents and consumed study beverages in assigned groups of three while their behavior was videotaped. Emotion was assessed via self-report as well as continuous coding of facial muscle movements. The relationship between self-reported and behaviorally expressed emotion diverged significantly across beverage conditions: positive affect: b = -0.174, t = -2.36, p = .022; negative affect, b = 0.4319, t = 2.37, p = .021. Specifically, self-reports and behavioral displays converged among sober but not intoxicated participants. Further, alcohol's effects on positive facial displays remained significant in models controlling for self-reported positive and negative emotion, with alcohol enhancing Duchenne smiles 20% beyond effects captured via self-reports, pointing to unique effects of alcohol on behavioral indicators of positive emotion. Findings highlight effects of acute intoxication on the convergence and divergence of emotion measures, thus informing our understanding of measures for capturing emotions that are most proximal to drinking and thus most immediately reinforcing of alcohol consumption. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Konsep Penyusunan Perencanaan Evaluasi Hasil Belajar

The first step that needs to be done in evaluation activities is to make a plan. This planning is important because it will affect the next steps, and even affect the effectiveness of the overall evaluation procedure. This study aims to discuss the planning that must be prepared in the evaluation of learning outcomes. The method used in this study is a library research approach, this data collection uses a method of searching from various sources such as journals and research that has been studied. The results of the study show that the steps that need to be taken in planning an evaluation of learning outcomes include (1) Formulating the objectives of the evaluation (2) Determining the aspects to be evaluated (3) Selecting and determining the techniques to be used in the evaluation (4) Preparing the evaluation tools to be used in the evaluation (5) Determining the evaluation benchmarks (6) Determining the frequency of evaluation activities (7) Presenting the test (8) Processing the test results (9) Reporting evaluation results, and (10) Use of evaluation results. Through this careful evaluation planning, it is possible to set behavioral objectives or indicators to be achieved, to prepare for the collection of data and information needed and to be able to use the right time.

Effects of the Stanford Chronic Conditions Model on Behavioral and Clinical Indicators in Saudi Arabia: A Prospective Quasi-Experimental Study.

This study aimed to evaluate the 6-month impact of the Chronic Disease Self-Management Program based on the Stanford chronic condition model on behavioral and clinical indicators in individuals with chronic illnesses. This prospective, quasi-experimental study was conducted in primary healthcare centers located in Riyadh, Saudi Arabia. A total of 110 adults aged 18 years or older, living with at least one chronic disease, and receiving treatment at a primary healthcare center were included. We compared patients who received the Chronic Disease Self-Management Program with those who received the usual care from primary healthcare centers. Data analysis included analysis of descriptive and covariance statistics. The analysis of covariance indicated that individuals who received the Chronic Disease Self-Management Program had significantly lower systolic (F=5.60, p<0.05) and diastolic blood pressure (F=7.60, p<0.01). These patients were more likely to adopt healthy behaviors to manage their chronic illnesses (F=11.17, p<0.01). However, no significant differences were observed in the HbA1c values of the patients. We recommend incorporating the Stanford Chronic Disease Self-Management Program into patient education to foster peer support for effective chronic disease management.

Graduate student literature review: Integrating concepts of animal welfare and health-related quality of life for pre-weaned dairy calves.

Dairy calf welfare assessment tools focusing on the pre-weaning period have been proposed in recent research. Despite the existence of these tools, assessing the welfare and health-related quality of life (HRQoL) of dairy calves remains challenging. These difficulties may stem from the complexity of assessing all dimensions of calf welfare and the validity, reliability, and feasibility of the indicators used in assessment tools. This review aims to discuss welfare and HRQoL concepts and integrate them into a framework to facilitate the understanding of dairy calf welfare and HRQoL. The review also identifies on-farm dairy calf welfare indicators and explores how their validity, reliability, and feasibility have been evaluated. HRQoL, as a component of the quality of life concept, is used to determine how an animal feels during illness using behavioral expressions of affective states. In this review, we adapted a human HRQoL framework for dairy calves, illustrating the interconnection of 3 domains of animal welfare (behavior, mental state and health) in assessing calves' HRQoL. It is worth noting that a limited number of HRQoL assessment tools have been developed for dairy calves, and there is no standard way to assess the welfare of pre-weaned dairy calves. Some studies focus on specific aspects of animal welfare, while others address this concept more broadly. While behavioral indicators have been explored in dairy science, they often remain disconnected from the concept of HRQoL. After reviewing various welfare assessment tools focusing on pre-weaned dairy calves, 44 welfare indicators were selected. Considering the selected welfare indicators, we observed that their validity, reliability, and feasibility have not been extensively explored. This review contributes to understanding welfare and HRQoL concepts for pre-weaned dairy calves and highlights opportunities to enhance the assessment of welfare and HRQoL for these animals.