Response Categories Research Articles

Probabilistic analysis of tumor growth inhibition models to Support trial design

A large enough sample size of patients is required to statistically show that one treatment is better than another. However, too large a sample size is expensive and can also result in findings that are statistically significant, but not clinically relevant. How sample sizes should be chosen is a well-studied problem in classical statistics and analytical expressions can be derived from the appropriate test statistic. However, these expressions require information regarding the efficacy of the treatment, which may not be available, particularly for newly developed drugs. Tumor growth inhibition (TGI) models are frequently used to quantify the efficacy of newly developed anticancer drugs. In these models, the tumor growth dynamics are commonly described by a set of ordinary differential equations containing parameters that must be estimated using experimental data.One widely used endpoint in clinical trials is the proportion of patients in different response categories determined using the Response Evaluation Criteria In Solid Tumors (RECIST) framework. From the TGI model, we derive analytical expressions for the probability of patient response to combination therapy. The probabilistic expressions are used together with classical statistics to derive a parametric model for the sample size required to achieve a certain significance level and test power when comparing two treatments.Furthermore, the probabilistic expressions are used to generalize the Tumor Static Exposure concept to be more suitable for predicting clinical response. The derivatives of the probabilistic expressions are used to derive two additional expressions characterizing the exposure and its sensitivity. Finally, our results are illustrated using parameters obtained from calibrating the model to preclinical data.

Development and Validation of the Youth Back Activity Questionnaire: A Measure of Function for Adolescents with Low Back Pain.

Psychometric study. To develop a reliable and valid patient-reported outcome measure to assess functional ability in adolescents with low back pain (LBP) using the Rasch model. LBP is quite common in adolescence. Currently, no method adequately measures the functional ability of adolescents with LBP. Widely used outcome measures for LBP are problematic in adolescents, with significant ceiling/floor effects. Two hundred adolescents (15.4 ± 1.9 years; 56% female) with LBP. This study was comprised of two parts: Part 1: Development of the Youth Back Activity Questionnaire (YouthBAQ) (n = 75), and Part 2: Psychometric evaluation of the newly designed YouthBAQ using the Rasch Model (n = 125). Participants also completed the Oswestry Disability Index (ODI) and Micheli Functional Scale (MFS), Data Analysis: Reliability and construct validity of the YouthBAQ were assessed using Rasch analyses. Correlations with the ODI and MFS were used to determine the convergent validity of the YouthBAQ. Part 1: Based on the results of the Rasch analysis, 31 items were reduced to 14. Part 2. The 14-item YouthBAQ demonstrated good reliability and construct validity, with good item fit (>0.7 and <1.3), acceptable item hierarchy and spacing, response category functioning, unidimensionality (ratio >3:1), local independence (<0.30). The MFS demonstrated unacceptable reliability (person reliability = 0.62) and was not compared against the YouthBAQ. The ODI demonstrated acceptable reliability (person reliability =0.70), but poor construct validity among adolescents with LBP. The YouthBAQ demonstrated a moderate correlation with the ODI (r = 0.4). The YouthBAQ is a quick and easily understood measure of function with good validity and reliability in adolescents with LBP. The YouthBAQ is a more appropriate measure of functional ability than the ODI or MFS in this population.

Diversity, Equity, and Inclusion Initiatives from Providers of Continuing Pharmacy Education

Abstract: Although continuing education (CE) plays an important role in educating health care providers on diversity, equity, and inclusion (DEI), little is known about the DEI initiatives promoted and implemented by providers of CE. Surveys were electronically administered to accredited pharmacy continuing education providers (CEPs) from 2021 to 2023. Survey items were assessed providers' current efforts and best practices for incorporating DEI into organizational culture and educational programming. Frequencies and percentages were calculated for categorical responses, and a general interpretivist approach was used to code open-ended response items and describe themes. Overall, 50 and 33 responses were received in 2021/2022 and 2023, respectively. The most common step taken by programs to advance DEI was to engage pharmacists and/or pharmacy technicians in DEI discussions. Qualitative analysis described categories and themes as follows: best practices (CE programming DEI strategies, organizational DEI strategies), DEI goals (mission statement and DEI strategic plan, developing CE, continuing pharmacy education committee and task force, and faculty development and guidance), and accrediting body support (sharing best practices and training and technical support). Although CEPs described current efforts and best practices related to DEI in CE, organizations involved in CE accreditation can continue to explore ways to support DEI initiatives.

Neither agree nor disagree: use and misuse of the neutral response category in Likert-type scales

Neither agree nor disagree: use and misuse of the neutral response category in Likert-type scales

Abstract B156: Exploring identity-specific stressors among Black women with a family history of cancer

Abstract Background: Compared to women of other racial/ethnic groups, Black women are more likely to be diagnosed with aggressive subtypes of breast cancer (including triple-negative breast cancer), diagnosed at younger ages, diagnosed at more advanced stages, and have higher mortality rates and poorer survival outcomes. Black women also experience higher rates of breast cancer recurrence, which further exacerbates the survival disparities. These disparities are influenced by a complex interplay of genetic, environmental, and social factors, including chronic stress and discrimination. Understanding identity-specific stressors will help identify potential risk factors impacting breast cancer outcomes among Black women.Objective: This study aims to identify common stressors among Black women aged 18 to 49 with a family history of cancer, exploring potential links to breast health risk. Methods: We conducted a grounded theory study, including qualitative interviews from Black women in the Augusta, Georgia, area (N=47) discussing life as a Black woman and sources of stress. Participants with a close family history of cancer (defined as having at least one first- or second-degree relative diagnosed with cancer) were selected for this analysis (n=22). We applied the Sort &amp; Sift, Think &amp; Shift method for analysis. Results: We identified three categories in response to the research question, including healthcare access barriers and medical invalidation (particularly in rural communities), intergenerational trauma, and Strong Black Woman persona (characterized by independence, emotional suppression, and a tendency to care for others while neglecting their own needs). Participants described these categories in reference to themselves and their female family members with cancer. Participants reported stress related to jobs and caregiving responsibilities, lack of adequate social support, lack of time and finances necessary for self-care, and racial discrimination in medical settings. Conclusion: The identified stressors highlight the complex interplay between familial cancer history and daily stress experiences among Black women. These findings suggest the need for targeted interventions to mitigate stress and potentially reduce breast cancer risk. Further research is warranted to explore these themes in relation to breast cancer outcomes. Citation Format: Samantha R. Jones, Debbynie Barsh, Kathleen McIntyre, Christy J.W. Ledford. Exploring identity-specific stressors among Black women with a family history of cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B156.

Development and validation of the 5-Dimension Comprehensive Assessment Scale (5DCAS) for assessing physical function and health in axial spondyloarthritis.

To describe the development and validation of a novel patient reported scale, which is a comprehensive assessment of the physical function and health specific for patients with axial spondyloarthritis (axSpA). This is a multiphase, mixed methods study. Based on opinion collection and discussions of multidisciplinary consensus meetings and patients, an initial item pool covering all of the ranges of functioning was generated. The item optimization, model fit, response category functioning, differential item functioning, reliability, structure validity, and unidimensionality were tested by confirmatory factor analysis and Rasch measurement theory framework. After the consensus meeting and the two rounds of surveys in patients with axSpA, the initial pool of 135 items was reduced to 25 items formed in five dimensions, which exhibited preferable item reliability, item fit, and person fit to the Rasch model. The Five-Dimensional Comprehensive Assessment Scale (5DCAS) had the best reliability and validity (Kaiser-Meyer-Olkin was 0.919, and the standardized Cronbach's α coefficient was 0.932). The final version of 5DCAS had good unidimensionality, and the Person Separation Index ranged from 0.77 to 0.85. 5DCAS significantly correlated with ASAS-HI, SF-36, BASFI, and disease activity with p values of < 0.001. 5DCAS is a novel patient-reported outcome specific to axSpA, and it forms five dimensions providing a linear sum score of 25 items. 5DCAS comprehensively and significantly represents the physical function and health status of patients with axSpA, although its performance needs further validation in future clinical practices. Key Points • The primary goal in the management of axial spondyloarthritis is to maximize health-related quality of life. Except for the current instruments of ASAS-HI, BASFI, or SF-36, the heterogeneous clinical symptoms and rapid updated treat-to-target concept require a new instrument which can comprehensive and significant evaluate the changes of physical function and health-related quality of life due to disease. • 5DCAS is a novel patient-reported outcome specific to axSpA, and it forms five dimensions providing a linear sum score of 25 items, which contained aspect of pain involvement, spine mobility, global body performance and activity, social participation and environment, and mental health. All of the items were set to a 4-point semantic rating scale measuring severity, frequency, or interference from score 0 to 3. Total 5DCAS score ranges from 0 to 75; higher scores represented greater symptom burden and worse physical function. • 5DCAS is a comprehensive, multidisciplinary, and convenient disease outcome measurement specific for axSpA. It provides a new evaluation instrument in clinical trial and treat-to-target clinical remission for patients and physicians, and also provides a sensitive and accurate assessment standard for optimized health benefits.

120 Cross-validation of breeding values and future phenotypes for heifer pregnancy in Red Angus cattle using the LR method

Abstract Heifer pregnancy (HP) in beef cattle is a heritable and economically relevant trait but is challenging to predict using best linear unbiased prediction (BLUP) methodology due to the binary nature of the phenotypes. The observed categorical responses (e.g., 1 = pregnant; 0 = non-pregnant) are due to an animal either exceeding a particular threshold for pregnancy or not on an underlying genetic distribution which is why this binary trait is commonly evaluated using a threshold model (TM). However, TM are susceptible to scenarios where only 1 category of observation is predominant. In this study we compare genetic evaluations including “partial” and “whole” data to cross-validate methodologies for the prediction of HP. The objectives of this study were then to evaluate estimators of prediction accuracy, bias, and dispersion. Heifer fertility data were obtained from the Red Angus Association of America. Evaluations for HP were performed using ASREML3.0 and a univariate, BLUP, traditional TM; and a linear continuous BLUP animal model (LM) on both reference (whole) and validation (partial) datasets. Reference data included all animals with HP phenotypes, and validation data had censored the most recent 5 yr of HP phenotypes (~16% of HP phenotypes). Reference and validation estimated breeding values (EBV) were then compared using linear regression (LR) methods. Heritability estimates were 0.05 (± 0.007) and 0.15 (± 0.009) for LM and TM respectively. The average BIF accuracy of censored validation animals (n = 10,175) using a LM on whole and partial datasets were 0.07 and 0.03 respectively, and using a TM on whole and partial datasets were 0.09 and 0.04 respectively. Comparing the mean EBV from the partial and whole datasets can be a good estimator of bias in the model as the difference is expected to be 0. The difference between the mean EBV from partial and mean EBV from whole datasets were –0.003 and –0.016 for LM and TM respectively, suggesting that the TM is a slightly more biased evaluation. The regression of EBV obtained with whole data on EBV estimated with partial data has an expected value of 1 if there is no over/under dispersion which can be a useful tool in determining model correctness and gauge bias in an evaluation. The closer a regression coefficient is to 1, the less biased the evaluation is. The slope of the regression of EBV estimated from whole data on partial data for validation animals were 0.98 (P &amp;lt; 0.0001) and 0.91 (P &amp;lt; 0.0001) for LM and TM respectively. Although both regression coefficients were above 0.90 suggesting both evaluations are performing well, these results suggest that TM models may be a more biased predictor of HP than LM despite a small benefit in prediction accuracy for the trait.

Domestic and Family Violence Screening and Response: A Prospective, Cross-Sectional, Mixed Methods Survey in Private Mental Health Clients.

Most domestic and family violence (DFV) research has focused on establishing prevalence and screening rates in public health and community samples. This study sought to address a gap in the literature by evaluating DFV screening and response practices in a private mental healthcare inpatient service and determining if clients of the service had unmet DFV needs. A prospective, convenience sample, mixed methods, cross-sectional survey of adult inpatient mental health consumers was employed. Sixty-two participants completed the Royal Melbourne Hospital Patient Family Violence Survey. Quantitative Likert-type and categorical responses were collated and analysed descriptively (count and percentage). Free-text responses were analysed using qualitative description within a content analysis framework. Sixty-five percent of participants had been screened for at least one DFV issue, on at least one occasion, with 35% not being screened, to their recall. Twenty-three percent reported disclosing DFV concerns, 82% felt very supported by the clinician's response to their disclosure, and 86% were provided with information they found helpful. Unmet needs were identified in 13% of participants, who had wanted to disclose DFV concerns but not feel comfortable to do so. No unscreened respondents disclosed DFV concerns, highlighting the need to uphold best practice guidelines for direct enquiry. Most disclosing clients were positive about the support they received. Indicated areas for improvement were screening rates, active follow-up, increasing psychology support levels and safety planning.

MRI Predicts Residual Disease and Outcomes in Watch-and-Wait Patients with Rectal Cancer.

Background MRI plays a crucial role in restaging locally advanced rectal cancer treated with total neoadjuvant therapy (TNT); however, prospective studies have not evaluated its ability to accurately select patients for nonoperative management. Purpose To evaluate the ability of restaging MRI to predict oncologic outcomes and identify imaging features associated with residual disease (RD) after TNT. Materials and Methods This was a secondary analysis of the Organ Preservation in Rectal Adenocarcinoma (OPRA) trial, which randomized participants from April 2014 to March 2020 with stages II or III rectal adenocarcinoma to undergo either induction or consolidation TNT. Participants enrolled in the OPRA trial who underwent restaging MRI were eligible for inclusion in the present study. Radiologists classified participants as having clinical complete response (cCR), near-complete clinical response (nCR), or incomplete clinical response (iCR) based on restaging MRI at a mean of 8 weeks ± 4 (SD) after treatment. Oncologic outcomes according to MRI response category were assessed using Kaplan-Meier curves. Logistic regression analysis was performed to identify imaging characteristics associated with RD. Results A total of 277 participants (median age, 58 years [IQR, 17 years]; 179 male) who were randomized in the OPRA trial had restaging MRI forms completed. The median follow-up duration was 4.1 years. Participants with cCR had higher rates of organ preservation compared with those with nCR (65.3% vs 41.6%, log-rank P < .001). Five-year disease-free survival for participants with cCR, nCR, and iCR was 81.8%, 67.6%, and 49.6%, respectively (log-rank P < .001). The MRI response category also predicted overall survival (log-rank P < .001), distant recurrence-free survival (log-rank P = .005), and local regrowth (log-rank P = .02). Among the 266 participants with at least 2 years of follow-up, 129 (48.5%) had RD. At multivariable analysis, the presence of restricted diffusion (odds ratio, 2.50; 95% CI: 1.22, 5.24) and abnormal nodal morphologic features (odds ratio, 5.04; 95% CI: 1.43, 23.9) remained independently associated with RD. Conclusion The MRI response category was predictive of organ preservation and survival. Restricted diffusion and abnormal nodal morphologic features on restaging MRI scans were associated with increased likelihood of residual tumor. ClinicalTrials.gov identifier: NCT02008656 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Milot in this issue.

Linguistic and cultural validation of symptom questionnaire for visual dysfunctions (SQVD) for psychometric analysis in the Chinese context.

The Spanish scale symptom questionnaire for visual dysfunctions (SQVD) was sinicized and tested for reliability and validity in the Chinese context, employing both classical measurement theory and item response theory. A meticulous translation was conducted using the modified Brislin translation model, with input from experts for cross-cultural debugging and in-depth review. Following a pre-survey study, the Chinese version of the SQVD was finalized. A convenience sampling method was used to select 270 patients from the target group and 252 valid questionnaires were successfully collected. The Rasch model was employed to assess response category functionality, fit statistics, unidimensionality, person and item reliability, separation, targeting, and differential item functioning. Classical test theory was applied to evaluate internal consistency and retest reliability, supplemented by correlation analysis. Job characteristic curves were also plotted to assess diagnostic accuracy. The Chinese SQVD conformed to a unidimensional structure with excellent reliability and validity. Person and item reliabilities were 0.85 and 0.99, respectively, indicating, high stability. Person and item separation indices were 2.37 and 11.54, respectively, signifying strong differentiation ability. Retest reliability was 0.917, further emphasizing the stability of the scale. The area under the receiver operating characteristic curve was 0.908 (95% CI: 0.854, 0.962), with a cutoff value of 7.5 and Youden index of 0.733, highlighting the scale's high diagnostic accuracy. The translated and culturally adapted Chinese SQVD demonstrated excellent psychometric properties. With streamlined items, short assessment time, and high efficiency, the scale is a stable and reliable clinical tool for detecting a variety of conditions related to refractive, regulatory, and binocular vision dysfunction.

Validating self-reported driving behaviours as determinants of real-world driving speeds

Self-reported driver behaviour has long been a tool used by road safety researchers to classify drivers and to evaluate the impact of interventions yet the relationship with real-world driving is challenging to validate due to the need for extensive, detailed observations of normal driving. This study examines this association by applying the large UDRIVE naturalistic driving study data involving 96 car drivers, comprising 131,462 trips and 1,459,110 km travelled over a duration of 32,096 hours, to compare individual questions and composite indicators based on the Driver Behaviour Questionnaire with real world driving. Self-reported speed behaviour was compared to the measured values under urban and highway conditions. Generalised Linear Mixed Models were developed to examine the relationships between the observed speed behaviours with DBQ errors and violations scores in conjunction with traffic and environmental factors. Drivers’ self-reported data on speed selection seldom aligned with their real-world behaviour and there were no meaningful differences between many of the response categories. The DBQ violations and errors scales showed a highly significant correlation with driving speed indicators however they had a low explanatory power compared to other traffic situational and driving factors. Overall, the study highlights the need to validate self-reported driving data against the accuracy and relevance to real-world driving.

How many grades of response categories does the commitment to the profession of medicine scale provide the most information?

In the present study, we examined the psychometric properties of the data obtained from the Commitment to Profession of Medicine Scale (CPMS) with 4-point, 5-point, 6-point, and 7-point response sets based on Item Response Theory (IRT). A total of 2150 medical students from 16 different universities participated in the study. The participants were divided into four groups consisting of 560, 544, 502, and 544 medical students. The first group (n=560) was assigned four-point, the second group (n=544) five-point, the third group (n=502) six-point, and the fourth group (n=544) seven-point Likert forms. We used R statistical software to analyze the data. The results of item calibrations conducted with the Graded Response Model (GRM) were analyzed. The results show that the eigenvalue increased from 4-point to 7-point. Similarly, the explained variance percentage and the scale's reliability increased gradually from 4-point to 7-point. The explained variance, reliability level, and eigenvalue were very close in the 5-point and 6-point forms.

Trajectory modeling and response prediction in transcranial magnetic stimulation for depression

BackgroundRepetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. MethodsWe compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. ResultsLCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC=0.70, 95 % CI=[0.52–0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC=0.76, 95 % CI=[0.58–0.94], but likewise, not before. ConclusionsIn showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

Interactive robots for personalised multimodal comedy experiments

This research proposes a novel system for personalising humour delivery using interactive robots with AI, natural language processing, and human-robot interaction technologies. The robots can adapt the multimodal content of jokes tactically now and strategically over time based on recording and analyzing each user’s subjective emotional reactions. The study evaluated pragmatic aspects of timing and framing jokes by having the robots try different “comeback tactics” and observing audience response. An ANOVA analysis revealed six distinct response categories. 78 % of participants positively rated the personalized robot comedy across the six scenarios. However, when both robot comedians took a negative tone, only 23 % approved (p < 0.001). Interestingly, audiences validated the robot comedian’s performance (89 % approval) more than the human comedian (54 % approval), a statistically significant difference (p < 0.01). These findings shed light on ideal characteristics for effective human-robot comedy interactions, highlighting the importance of topicality, maintaining a generally positive mood, and the unique strengths of robot performers. The study paves the way for further optimization of personalized, adaptive robot humour systems leveraging multimodal AI capabilities to enhance entertainment experiences tailored to individuals.

Response processes for patients providing quantitative self-report data: a qualitative study.

To identify factors that influence response processes for patients providing quantitative self-report data. Secondly, due to the lack of integrative and explanatory models in this area, to develop a model of patients' response processes that can guide what to look for when considering validity evidence and interpreting scores on individual items. Participants (n = 13) were recruited from a specialized substance use disorder treatment clinic and interviewed while responding to items from a clinical feedback system implemented for routine outcome monitoring in that setting. The interview approach was based on cognitive interviewing. Data collection and analysis were inspired by a grounded theory approach. We identified several variables that influenced the participants' response processes. The variables were organized into five categories: context-related variables; item-related variables; response base variables; reasoning strategies; and response selection strategies. We also found that the participants' responses for many items were affected by different aspects of the response process in ways that are relevant to interpretation but not necessarily discernible from the numerical scores alone, and we developed response categories to capture this. The findings suggest that patients providing quantitative self-report data encounter conditions in the response process that challenge and influence their ability to convey meaning and accuracy. This results in responses that for many of the items reflect messages important for interpretation and follow-up, even if it does not appear from the numerical scores alone. The proposed model may be a useful tool when developing items, assessing validity, and interpreting responses.

Viscoelastic Reversibility of Carrageenan Hydrogels under Large Amplitude Oscillatory Shear: Hybrid Carrageenans versus Blends.

The viscoelastic response of carrageenan hydrogels to large amplitude oscillatory shear (LAOS) has not received much attention in the literature in spite of its relevance in industrial application. A set of hybrid carrageenans with differing chemical compositions are gelled in the presence of KCl or NaCl, and their nonlinear viscoelastic responses are systematically compared with mixtures of kappa- and iota-carrageenans of equivalent kappa-carrageenan contents. Two categories of LAOS response are identified: strain softening and strain hardening gels. Strain softening gels show LAOS non-reversibility: when entering the nonlinear viscoelastic regime, the shear storage modulus G' decreases with increasing strain, and never recovers its linear value G0 after successive LAOS sweeps. In contrast to this, strain hardening carrageenan gels show a certain amount of LAOS reversibility: when entering the nonlinear regime, G' increases with strain and shows a maximum at strain γH. For strains applied below γH, G0 shows good reversibility and the strain hardening behavior is maintained. For strains larger than γH, G0 decreases significantly indicating an irreversible structural change in the elastic network. Strain hardening and elastic recovery after LAOS prevail for hybrid carrageenan and iota-carrageenan gels, but are only achieved when blends are gelled in NaCl, suggesting a phase separated structure with a certain degree of co-aggregated interface for mixed gels.

A Novel Job Similarity Index for Career Transition in the Mining Industry

In this study, with the primary goal of capturing ongoing digital transformation and automation impacts on the mining industry and its workforce, we conduct several interviews with mining industry experts in the USA and analyze our survey reports qualitatively and quantitatively through exploratory analysis. After the interpretation of the insights of industry experts, we proceed to generate a personalized and customized data analysis through a novel metric based on skills, knowledge, competencies, and occupational requirements, which quantifies the job similarities for occupations in the mining industry based on the publicly available database of the United States Department of Labor. We utilize text analytics to tokenize and classify the interviews to capture a better understanding of major response categories. The temporal analysis shows that the critical competency needs in the data science and autonomy category increases from 28% in current demands to 43%. In defining our metric, we also calculate Kullback–Leibler (KL) divergence for each job profile that enables determining whether and to what extent that job is transitionary in our test set based on the mean, standard deviation, and kurtosis of each job of interest. Our analysis reveals that the in-group job transitions are significantly easier than the between-group transitions, proving our initial assumptions and common sense. The generated heat maps provide the opportunity to present the gap between the current job and desired job profiles that provide feasible career change options, among others, offering individualized career paths for job seekers and promoting potential job transitions. Through the collection of industry-specific individual employee data, the AI system is envisaged to continue to learn as end users engage with the system, thus creating a central data hub specifically for the future workforce in the mining industry. Although the study has limitations on generalizability for qualitative assessments, it presents itself as a valuable application of how qualitative and quantitative approaches could be of value for future worker training in the mining sector.

Adaptation and validation of the All Aspects of Health Literacy Scale (AAHLS) in healthcare settings of Addis Ababa, Ethiopia

BackgroundSince health literacy is known to be related to health outcomes, it should be measured to explain how it is associated with the health status of the population. Health literacy tools are designed to measure different dimensions of health literacy of individuals based on their objective. The AAHLS tool is comprehensive and can cover all aspects of health literacy. Overall in Ethiopia, there is no standard health literacy tool that has been developed or/and validated. Therefore, the aim of this study was to adapt and validate the All Aspects of Health Literacy Scale (AAHLS) in healthcare facilities in Addis Ababa city, Ethiopia.MethodsA mixed-method, facility-based, cross-sectional study was conducted in Addis Ababa city from February 1, 2022, to May 30, 2022. The study was conducted in three phases: forward and backward translation and expert review, cognitive interviews and survey administration. For the cognitive interviews, a total of 16 participants and for the survey administration, 199 participants were involved. Coding and analysis of the qualitative data were performed using OpenCode 4.03 computer software. Then, pretesting (survey administration) was conducted to check the validity and reliability of the tool. CFA was conducted using SPSS version 26 and Stata version 14.Results and discussionThe original three response categories were revised to five response categories based on the cognitive interview findings and expert reviews. The survey was administered to 199 participants, 55.8% of whom were males. The Kaiser‒Meyer‒Olkin measure of sampling adequacy was 0.685, with a significant difference according to Bartlett’s test of sphericity (p < 0.001). After removing the empowerment factor model fit indices, the internal reliability and convergent and divergent validities improved. Confirmatory factor analysis showed that the model fit indices of the tool were satisfactory. The overall internal consistency, Cronbach’s alpha, was 0.71.ConclusionThe three response categories of the tool were revised to five response categories. The AAHLS tool was revised to include 10 items. The tool has exhibited adequate model fitness. The validated tool can be used for future health literacy assessments and interventions.

Activities-specific balance confidence scale: a Rasch evaluation of the Arabic version in lower-limb prosthetic users

Purpose To evaluate the psychometric properties of the Arabic version of the Activities-Specific Balance Confidence Scale using the 5-option response categories for individuals with lower limb amputation (ABC-5/Ar). Materials and methods This was a methodological study on a convenience sample of individuals with unilateral lower-limb amputation attending outpatient rehabilitation centres in Saudi Arabia and Turkey (N = 155). Rasch analysis (WINSTEPS version 4.6.5) was used to examine the 5-categories rating scale structure, item fit, item difficulty hierarchy, person separation index, unidimensionality, local item dependency, and differential item functioning. Results The ABC-5/Ar 5-response option demonstrated an appropriate model fit. Most items fit the Rasch model, except for item #12 “walk in a crowded mall,” which showed an overfitting value of 0.63. The person separation indices 2.95 (Cronbach’s α = 0.96). Principal component analysis of residuals confirmed the unidimensionality of the scale; however, local dependency was detected between item #14 “Ride in escalator holding rail,” and item #15 “Ride in escalator not holding rail.” Conclusions The findings suggest that the ABC-5/Ar shows promise in assessing balance confidence in Arabic-speaking lower-limb prosthesis users. However, further studies with larger sample sizes and in diverse clinical contexts are needed to confirm its effectiveness in various clinical settings.

ANALISIS FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI INDONESIA MENGGUNAKAN MODEL REGRESI LOGISTIK BINER

The primary tool for assessing the extent of human development progress in a country is the Human Development Index (HDI). There are three components of Indonesia's Human Development Index (HDI). The method used to characterize the quality of human existence is based on these foundational aspects of HDI. The three elements include the role of economic advancement in human progress, as well as health, knowledge, and a decent standard of living. The objective of this research is to conduct binary logistic regression modeling to identify the key aspects that influence the Human Development Index of Regencies and Cities in Indonesia. If the response variable is binary and the predictor factors consist of one or more continuous or categorical variables, binary logistic regression is the statistical technique used to model the categorical response variable. The research results indicate that the percentage of Life Expectancy (X1), Average Length of Schooling (X2), Expected Years of Schooling (X3), and Per Capita Expenditure (X4), both partially and simultaneously, are independent variables that have the most significant impact on HDI at a real level of α = 5%. A balanced accuracy rating of 91.83% was achieved from the model evaluation, indicating that the model is useful