Yoxas/statistical_literacy huggingface dataset:
The aims of this systematic review were to explore the pooled prevalence of multidimensional frailty assessed by the Tilburg Frailty Indicator among community-dwelling older adults. A systematic review and meta-analysis. A comprehensive literature search was conducted across multiple databases, including PubMed, Web of Science, Embase, the Cochrane Library, CINAHL and three Chinese databases. Two independent researchers selected the literatures, extracted the data and evaluated the quality. All statistical analyses were performed using STATA version 16.0. There were 66 studies with a total of 40,597 individuals that were eligible for the meta-analysis. Data from the meta-analysis revealed the pooled prevalence of 42 % for multidimensional frailty (95 % CI: 38 %-45 %, I2 = 98.9 %, T2 = 0.024, p < 0.001). Among the six studies that provided data for different age groups, the results demonstrated an increasing trend in the prevalence of multidimensional frailty with advancing age. The results of gender-stratified analysis proved that the pooled prevalence of multidimensional frailty in women (45 %, 95 % CI: 39 %-51 %, p < 0.001) was higher than that in men (33 %, 95 % CI: 28 %-39 %, p < 0.001). Based on different education levels, the prevalence of multidimensional frailty is highest in the primary elementary or illiterate group (41 %, 95 % CI: 30 %-52 %, p < 0.001). According to different marital status types, the pooled prevalence of multidimensional frailty in the married group was significantly lower (36 %, 95 % CI: 28 %-43 %) than that in the unmarried, divorced or widowed group (51 %, 95 % CI: 37 %-65 %). Through a comprehensive review, we identified that 42 % of elderly individuals living in communities exhibit multidimensional frailty, indicating that multidimensional frailty is relatively common in this population. Stratified analysis revealed that advanced age, female gender, lower education level and unmarried status were associated with higher rates of multidimensional frailty.
The aims were to study the sustainability of labour-market participation five years after an incident diagnosis of attention-deficit/hyperactivity disorder (ADHD) among young adults with gainful employment, and to examine the impact of sociodemographic-, work- and health-related factors on these findings. Swedish registers identified 2517 individuals, 19-29 years old, with an incident diagnosis of ADHD and gainful employment during 2006-2011. Labour-market participation was measured by the core-peripheral model, a model that measures the connection to the labour market from a weak connection (peripheral) to a strong connection (core). Sequence analysis analysed clusters of labour-market participation, from one year before and up to five years after diagnosis. Odds ratios (OR) with 95% confidence intervals (CI) between sociodemographic factors, comorbid disorders, and the identified clusters were analysed by multinomial logistic regression. Five clusters of labour-market participation were identified: 60% of individuals belonged to a cluster that maintained labour-market participation throughout the study period (core, close to core); 20% belonged to a cluster with a transition to a weak connection to the labour market (close to peripheral, peripheral); and 20% belonged to a cluster with "middle" labour-market participation, characterised by having long periods of sick leave and unemployment. Individuals with elementary school as highest attained education (OR:4.03;CI:2.35-6.93), comorbid mental disorders (OR:2.77;CI:2.10-3.66), or living in villages/small cities (OR:1.77;CI:1.25-2.51) were most likely to belong to a cluster transitioning towards a "peripheral" labour-market participation. Men were less likely to have peripheral labour-market participation than women (OR:0.55;CI:0.40-0.75). Over half of working individuals with ADHD maintain a strong attachment to the labour market several years after their first diagnosis of ADHD. Therefore, it is important to target those who have problems maintaining a position in the labour market, including women, those with low educational levels, and those living outside large cities.
The Alaska Native Tribal Health Consortium (ANTHC) participated in the United States Centers for Disease Control and Prevention Colorectal Cancer Control Program (CRCCP) from 2009 to 2015. We conducted a descriptive evaluation of ANTHC CRCCP demographics, quality measures, and clinical outcomes, including screening methods employed within the program and screening outcomes. There were 6981 program screenings completed, with the majority (81.3%) of people screened in the 50-75 year age group. Colonoscopy was the primary screening test used, accounting for 6704 (96.9%) of the screening tests. Quality of colonoscopy was high: adequate bowel preparation was reported in 98.2% of colonoscopies, cecal intubation rate was 98.9%, and the adenoma detection rate was 38.9%. A high proportion (58.9%) of colonoscopies had an initial finding of polyps or lesions suspicious for cancer; 41.2% of all colonoscopies had histological confirmation of either adenomatous polyps (40.6%) or cancer (0.5%). The ANTHC CRCCP successfully increased CRC screening among American Indian and Alaska Native peoples living in Alaska; this was achieved primarily through high-quality colonoscopy metrics. These data support a continued focus by the Alaska Native Tribal Health Consortium and its tribal health partners on increasing CRC screening and reducing cancer mortality among Alaska Native peoples.
The All of Us Research Program collects data from multiple information sources, including health surveys, to build a national longitudinal research repository that researchers can use to advance precision medicine. Missing survey responses pose challenges to study conclusions. We describe missingness in All of Us baseline surveys. We extracted survey responses between May 31, 2017, to September 30, 2020. Missing percentages for groups historically underrepresented in biomedical research were compared to represented groups. Associations of missing percentages with age, health literacy score, and survey completion date were evaluated. We used negative binomial regression to evaluate participant characteristics on the number of missed questions out of the total eligible questions for each participant. The dataset analyzed contained data for 334,183 participants who submitted at least one baseline survey. Almost all (97.0%) of the participants completed all baseline surveys, and only 541 (0.2%) participants skipped all questions in at least one of the baseline surveys. The median skip rate was 5.0% of the questions, with an interquartile range (IQR) of 2.5% to 7.9%. Historically underrepresented groups were associated with higher missingness (incidence rate ratio (IRR) [95% CI]: 1.26 [1.25, 1.27] for Black/African American compared to White). Missing percentages were similar by survey completion date, participant age, and health literacy score. Skipping specific questions were associated with higher missingness (IRRs [95% CI]: 1.39 [1.38, 1.40] for skipping income, 1.92 [1.89, 1.95] for skipping education, 2.19 [2.09-2.30] for skipping sexual and gender questions). Surveys in the All of Us Research Program will form an essential component of the data researchers can use to perform their analyses. Missingness was low in All of Us baseline surveys, but group differences exist. Additional statistical methods and careful analysis of surveys could help mitigate challenges to the validity of conclusions.
The American Academy of Pediatrics recommends annual testing in children beginning at 3 years of age to detect vision problems and prevent amblyopia. However, rates of vision testing in children from non-English primary language (NEPL) households are not well delineated. This study analyzed the 2018-2020 National Survey of Children's Health to examine patterns and predictors of vision testing among children from NEPL households. In this nationally representative cohort of 89,697 children 3-17 years of age, 70.9% of children received vision testing during the previous 12 months. Children from non-English-speaking households were less likely to undergo vision testing (64.3% vs 72.0%; aOR [95% CI] = 0.83 [0.72-0.95], P = 0.008). Decreased vision testing among children from NEPL households was driven by lower rates of testing at school (16.1% vs 21.0%; 0.72 [0.57-0.89], P = 0.009) or from an ophthalmologist or optometrist (49.0% vs 54.0%; 0.72 [0.61-0.85], P = 0.0004), whereas children from NEPL households were more likely to receive vision testing at health clinics (14.4% vs 3.1%; 3.25 [2.40-4.39], P < 0.0001). No differences were observed in rates of testing by a pediatrician (41.1% vs 44.0%; 1.05 [0.89-1.23], P = 0.69). Interventions to improve language services and health literacy are warranted to increase rates of vision testing among children from NEPL households.
The American Academy of Pediatrics recommends screening for unmet social needs, and the literature on inpatient screening implementation is growing. Our aim was to use quality improvement methods to implement standardized social needs screening in hospitalized pediatric patients. We implemented inpatient social needs screening using the Model for Improvement. An interprofessional team trialed interventions in a cyclical manner using plan-do-study-act cycles. Interventions included a structured screening questionnaire, standardized screening and referrals workflows, electronic health record (EHR) modifications, and house staff education, deliberate practice, and feedback. The primary outcome measure was the percentage of discharged patients screened for social needs. Screening for social needs was defined as a completed EHR screening questionnaire or a full social work evaluation. Process and balancing measures were collected to capture data on screening questionnaire completion and social work consultations. Data were plotted on statistical process control charts and analyzed for special cause variation. The mean monthly percentage of patients screened for social needs improved from 20% at baseline to 51% during the intervention period. Special cause variation was observed for the percentage of patients with completed social needs screening, EHR-documented screening questionnaires, and social work consults. Social needs screening during pediatric hospitalization can be implemented by using quality improvement methods. The next steps should be focused on sustainability and the spread of screening. Interventions with greater involvement of interdisciplinary health care team members will foster process sustainability and allow for the spread of screening interventions to the wider hospitalized pediatric population.
The American Association of Colleges of Nursing (AACN) released updated nursing curriculum Essentials in 2021. The new Essentials document reflects an innovative and unique approach to nursing education and provides a framework for competency-based education and assessment to prepare students to work in a profession that is ever-changing. The first in the state of Oregon, a Masters Entry into Professional Nursing program was launched with a curriculum based on the new Essentials with the goal to remain true to the program's current concept-based approach while incorporating elements of a competency-based curriculum. As with all new programs, curricular design is paramount and requires careful planning to ensure the curriculum aligns with education trends, meets the needs of diverse learners, adheres to regulatory requirements and standards, and has strong faculty buy-in. Curriculum development done in a systematic fashion with faculty input is imperative. All current faculty were invited by the school of nursing (SON) leadership to participate in the curriculum development process. Regularly scheduled meetings were held, and all interested faculty participants were able to provide input. This process also included the SON Curriculum Committee and SON Faculty Council, to ensure all faculty were included in the process and appraised of the curriculum development. The faculty members who participated in the systematic development process then served as advocates for the new curriculum and helped create a smooth transition when the new MEPN program was introduced.
The American healthcare system is ill-fitted to meet the needs of the growing population with Alzheimer's and Related Dementias (ADRD) and their caregivers, which disproportionately affects Latinos. This paper describes the caregiving process for Latino patients with ADRD from the perspective of caregivers and service providers to reveal service areas in need of improvement. Ten providers and 24 Latino caregivers were interviewed through the Miami-Dade County Aging Network. Interviews were analyzed using a grounded theory approach. Five themes emerged: lack of social service connectivity, case management challenges, unmet mental health needs, language barriers, and the need for alternative service models. Our analysis indicates insufficient training and the need to improve dementia competencies, support strategies, and understanding of cultural nuances among service providers. These findings can help inform the development of a required ADRD provider education curriculum that incorporates cultural competency training to improve the quality and effectiveness of care.
The American Nurses Credentialing Center's (ANCC's) Practice Transition Accreditation Program (PTAP) establishes standards for nurse residency programs to elevate and optimize the skills, knowledge, and attitudes of new nurses participating in nurse residency programs. Evidence-based practice (EBP) is foundational to providing safe nursing care. One of the National Academy of Medicine's (NAM's) 2020 goals stated that 90% of clinical decisions would be supported by the best available evidence to attain the best patient outcomes. Nurse residency programs can benefit from evidence-based strategies to develop EBP competencies in new nurses. The purpose of this scoping review was to synthesize the literature around strategies for incorporating EBP into nurse residency programs across the United States. This scoping review was informed by the JBI (formerly known as the Joanna Briggs Institute) methodology for scoping reviews. Searches were conducted by a health science librarian in PubMed and CINAHL with Full Text. Keywords and their synonyms, Medical Subject Headings (MeSH; PubMed), and Subject Headings (CINAHL with Full Text) were used. Covidence, a literature review management program, was used to organize the literature and manage the review. Title, abstract, and full-text reviews were completed within Covidence using three teams of two independent reviewers. Four hundred and thirty-eight citations were imported into Covidence. Ten articles were retained for the final review. Three strategies for incorporating EBP into nurse residency programs emerged from the literature: (1) exposure of nurse residents to existing organizational resources, (2) completion of online EBP modules, and (3) completion of an EBP project. The incorporation of EBP competencies in nurse residency programs aligns with NAM's and ANCC's goals, yet a paucity of evidence exists to guide curriculum development in nurse residency programs. This scoping review corroborates the need for further research to inform best practices for implementing EBP into nurse residency programs.
The American Society for Metabolic and Bariatric Surgery (ASMBS) Fellowship Certificate was created to ensure satisfactory training and requires a minimum number of anastomotic cases. With laparoscopic sleeve gastrectomy becoming the most common bariatric procedure in the United States, this may present a challenge for fellows to obtain adequate numbers for ASMBS certification. To investigate bariatric fellowship trends from 2012 to 2019, the types, numbers, and approaches of surgical procedures performed by fellows were examined. Academic training centers in the United States. Data were obtained from Fellowship Council records of all cases performed by fellows in ASMBS-accredited bariatric surgery training programs between 2012 and 2019. A retrospective analysis using standard descriptive statistical methods was performed to investigate trends in total case volume and cases per fellow for common bariatric procedures. From 2012 to 2019, sleeve gastrectomy cases performed by all Fellowship Council fellows nearly doubled from 6,514 to 12,398, compared with a slight increase for gastric bypass, from 8,486 to 9,204. Looking specifically at bariatric fellowships, the mean number of gastric bypass cases per fellow dropped over time, from 91.1 cases (SD = 46.8) in 2012-2013 to 52.6 (SD = 62.1) in 2018-2019. Mean sleeve gastrectomy cases per fellow increased from 54.7 (SD = 31.5) in 2012-2013 to a peak of 98.6 (SD = 64.3) in 2015-2016. Robotic gastric bypasses also increased from 4% of all cases performed in 2012-2013 to 13.3% in 2018-2019. Bariatric fellowship training has seen a decrease in gastric bypasses, an increase in sleeve gastrectomies, and an increase in robotic surgery completed by each fellow from 2012 to 2019.
The American Society for Metabolic and Bariatric Surgery (ASMBS) textbook serves as a comprehensive resource for bariatric surgery, covering recent advancements and clinical questions. Testing artificial intelligence (AI) engines using this authoritative source ensures accurate and up-to-date information and provides insight in its potential implications for surgical education and training. To determine the quality and to compare different large language models' (LLMs) ability to respond to textbook questions relating to bariatric surgery. Remote. Prompts to be entered into the LLMs were multiple-choice questions found in "The ASMBS Textbook of Bariatric Surgery, second Edition. The prompts were queried into 3 LLMs: OpenAI's ChatGPT-4, Microsoft's Bing, and Google's Bard. The generated responses were assessed based on overall accuracy, the number of correct answers according to subject matter, and the number of correct answers based on question type. Statistical analysis was performed to determine the number of responses per LLMs per category that were correct. Two hundred questions were used to query the AI models. There was an overall significant difference in the accuracy of answers, with an accuracy of 83.0% for ChatGPT-4, followed by Bard (76.0%) and Bing (65.0%). Subgroup analysis revealed a significant difference between the models' performance in question categories, with ChatGPT-4's demonstrating the highest proportion of correct answers in questions related to treatment and surgical procedures (83.1%) and complications (91.7%). There was also a significant difference between the performance in different question types, with ChatGPT-4 showing superior performance in inclusionary questions. Bard and Bing were unable to answer certain questions whereas ChatGPT-4 left no questions unanswered. LLMs, particularly ChatGPT-4, demonstrated promising accuracy when answering clinical questions related to bariatric surgery. Continued AI advancements and research is required to elucidate the potential applications of LLMs in training and education.
The amount of non-critical Emergency Medical Services missions has been increasing. After examination and treatment, paramedics can decide, based on guidelines, not to convey the patient to a healthcare facility. This study aimed to investigate patients' experiences in non-conveyance situations in Southwest Finland. Our research questions were: 1) Which patient concerns were not addressed? 2) What key actions improved patient comfort? and 3) What possible enhancements could make the patient experience more positive? This was a qualitative survey study. In March 2023, all the patients who met the inclusion criteria (N = 1017) received a survey via mail. The data from three open-ended questions were analyzed using inductive content analysis. The response rate was 22.2 % (n = 226). The unaddressed patient concerns were related to inadequate immediate care and guidance and non-clinical factors causing concern. Key actions for improved patient comfort were related to immediate treatment and guidance, as well as non-clinical factors that impact the patient experience. More thorough treatment processes and more attentive encounters would have enhanced the patient experience. Predominantly, patients reported having received excellent services. Utilizing paramedics' soft skills and keeping the patient as the central focus is key to improving the non-conveyance process and experience.
The analysis of electronic medical records at scale to learn from clinical experience is currently very challenging. The integration of artificial intelligence (AI), specifically foundational large language models (LLMs), into an analysis pipeline may overcome some of the current limitations of modest input sizes, inaccuracies, biases, and incomplete knowledge bases. To explore the effectiveness of using an LLM for generating realistic clinical data and other LLMs for summarizing and synthesizing information in a model system, simulating a randomized clinical trial (RCT) in epilepsy to demonstrate the potential of inductive reasoning via medical chart review. An LLM-generated simulated RCT based on a RCT for treatment with an antiseizure medication, cenobamate, including a placebo arm and a full-strength drug arm, evaluated by an LLM-based pipeline versus a human reader. Simulation based on realistic seizure diaries, treatment effects, reported symptoms and clinical notes generated by LLMs with multiple different neurologist writing styles. Simulated cohort of 240 patients, divided 1:1 into placebo and drug arms. Utilization of LLMs for the generation of clinical notes and for the synthesis of data from these notes, aiming to evaluate the efficacy and safety of cenobamate in seizure control either with a human evaluator or AI-pipeline. The AI and human analysis focused on identifying the number of seizures, symptom reports, and treatment efficacy, with statistical analysis comparing the 50%-responder rate and median percentage change between the placebo and drug arms, as well as side effect rates in each arm. AI closely mirrored human analysis, demonstrating the drug's efficacy with marginal differences (<3%) in identifying both drug efficacy and reported symptoms. This study showcases the potential of LLMs accurately simulate and analyze clinical trials. Significantly, it highlights the ability of LLMs to reconstruct essential trial elements, identify treatment effects, and recognize reported symptoms, within a realistic clinical framework. The findings underscore the relevance of LLMs in future clinical research, offering a scalable, efficient alternative to traditional data mining methods without the need for specialized medical language training.