bluuebunny/crossref_metadata_2025_split huggingface dataset:
PENINGKATAN KEMAMPUAN GURU MENYUSUN RENCANA KEGIATAN HARIAN BERBASIS BUDAYA MELALUI KEGIATAN SUPERVISI AKADEMIK DAN PENDAMPINGAN DI TK BINAAN PADA SEMESTER 1 TAHUN 2016/2017
Authors: Sukarmi - 
Published: March 2020

Penelitian ini bertujuan untuk mengamati kemampuan guru dalam mengaplikasikan rencana kegiatan harian berbasis budaya. Penelitian dilaksanakan di TK binaan Kecamatan Wirobrajan dan ikuti sebanyak 12 guru. Model penelitian yang digunakan adalah penelitian tindakan kelas. Metode yang digunakan adalah angket dan wawancara. Analisis yang digunakan adalah teknik deskriptif kuantitatif. Hasil yang diperoleh dari siklus satu menunjukkan bahwa peserta yang mencapai hasil cukup 4 orang atau 33,33%, dan memperoleh hasil baik 8 orang atau 66,67% di bidang ketrampilan. Sejumlah 7 orang atau 58,33 % nilai cukup dan 5 orang dengan nilai baik atau 41,67% di bidang pengetahuan. Nilai cukup 5 orang atau 41,67% dan 7 orang atau 58,33% memperoleh nilai baik pada nilai sikap. Hasil yang diperoleh dari siklus dua yaitu sesi ketiga pendalaman materi dan sesi keempat pemberian tugas dan evaluasi menunjukkan bahwa peserta yang mencapai hasil baik di bidang ketrampilan sejumlah 10 orang atau 83,33%, 9 orang atau 75% baik dari nilai pengetahuan dan 9 orang atau 75% baik dalam nilai sikap. Kata kunci: penyusunan rencana kegiatan harian berbasis budaya, supervisi akademik.
A Mobile Health Coaching Intervention for Controlling Hypertension: Single-Arm Pilot Pre-Post Study
Authors: Weerahandi Himali , Paul Soaptarshi , Quintiliani Lisa M , Chokshi Sara , Mann Devin M 
Published: March 2020

Background
The seminal Dietary Approaches to Stopping Hypertension (DASH) study demonstrated the effectiveness of diet to control hypertension; however, the effective implementation and dissemination of its principles have been limited.

Objective
This study aimed to determine the feasibility and effectiveness of a DASH mobile health intervention. We hypothesized that combining Bluetooth-enabled data collection, social networks, and a human coach with a smartphone DASH app (DASH Mobile) would be an effective medium for the delivery of the DASH program.

Methods
We conducted a single-arm pilot study from August 2015 through August 2016, using a pre-post evaluation design to evaluate the feasibility and preliminary effectiveness of a smartphone version of DASH that incorporated a human health coach. Participants were recruited both online and offline.

Results
A total of 17 patients participated in this study; they had a mean age of 59 years (SD 6) and 10 (60%) were women. Participants were engaged with the app; in the 120 days of the study, the mean number of logged blood pressure measurements was 63 (SD 46), the mean number of recorded weight measurements was 52 (SD 45), and participants recorded a mean of 55 step counts (SD 36). Coaching phone calls had a high completion rate (74/102, 73%). The mean number of servings documented per patient for the dietary assessment was 709 (SD 541), and patients set a mean number of 5 (SD 2) goals. Mean systolic and diastolic blood pressure, heart rate, weight, body mass index, and step count did not significantly change over time (P>.10 for all parameters).

Conclusions
In this pilot study, we found that participants were engaged with an interactive mobile app that promoted healthy behaviors to treat hypertension. We did not find a difference in the physiological outcomes, but were underpowered to identify such changes.
Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset
Authors: Caldwell Wendy K , Fairchild Geoffrey , Del Valle Sara Y 
Published: March 2020

Background
Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics.

Objective
This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions.

Methods
We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data.

Results
Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level.

Conclusions
These results demonstrate that internet data may be able to complement traditional influenza surveillance in some cases but not in others. Specifically, our results show that the CDC website traffic may inform national- and division-level models but not models for each individual state. In addition, our results show better agreement when the data were broken up by seasons instead of aggregated over several years. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream.
Bridging the Gap in Community Care for Patients With Borderline Personality Disorder: Protocol for Qualitative Inquiry Into Patient, Caregiver, and Clinician Perspectives on Service Gaps and Potential Solutions for Severe Emotion Dysregulation
Authors: Friesen Laura , Gaine Graham , Klaver Ellen , Klingle Kirsten , Parmar Devashree , Hrabok Marianne , Kelland Jill , Surood Shireen , Agyapong Vincent 
Published: March 2020

Background
Borderline personality disorder (BPD) is characterized by severe emotion dysregulation that is often complicated by comorbid diagnoses, deliberate self-harm, and chronic suicidal ideation. Unfortunately, current care pathways for individuals with BPD are strained by limited resources, inadequate training, and an overuse of emergency departments and crisis teams. Such barriers result in delayed access to effective treatment, which increases risk of deterioration, disability, and morbidity. A first step toward addressing these limitations of the current care pathway is to understand key stakeholders’ lived experiences in this pathway and their perspectives on potential solutions.

Objective
The purpose of this paper is to present a protocol for a study that explores the lived experiences of the current care pathway from the perspectives of patients with BPD, as well as their caregivers and clinicians.

Methods
A qualitative approach is most appropriate for the exploratory nature of the research objective. Accordingly, 3 to 6 patients with a diagnosis of BPD, 3 caregivers of individuals with BPD, and 3 clinicians of patients diagnosed with BPD will be invited to participate in individual, semistructured interviews that focus on service experiences.

Results
It is anticipated that results will yield insight into the lived experiences of patients with BPD, caregivers, and clinicians and provide a better understanding of the perceived gaps in services and potential solutions. Results are expected to be available in 12 months.

Conclusions
This paper describes a protocol for a qualitative study that seeks to understand the lived experiences and perspectives of key stakeholders (patients, caregivers, and clinicians) on the current care pathway for BPD. Results will provide a basis for future research in this area and will have the potential to inform training, practice, and policy.

International Registered Report Identifier (IRRID)
DERR1-10.2196/14885
Summarizing Complex Graphical Models of Multiple Chronic Conditions Using the Second Eigenvalue of Graph Laplacian: Algorithm Development and Validation
Authors: Faruqui Syed Hasib Akhter , Alaeddini Adel , Chang Mike C , Shirinkam Sara , Jaramillo Carlos , NajafiRad Peyman , Wang Jing , Pugh Mary Jo 
Published: March 2020

BackgroundIt is important but challenging to understand the interactions of multiple chronic conditions (MCC) and how they develop over time in patients and populations. Clinical data on MCC can now be represented using graphical models to study their interaction and identify the path toward the development of MCC. However, the current graphical models representing MCC are often complex and difficult to analyze. Therefore, it is necessary to develop improved methods for generating these models.ObjectiveThis study aimed to summarize the complex graphical models of MCC interactions to improve comprehension and aid analysis.MethodsWe examined the emergence of 5 chronic medical conditions (ie, traumatic brain injury [TBI], posttraumatic stress disorder [PTSD], depression [Depr], substance abuse [SuAb], and back pain [BaPa]) over 5 years among 257,633 veteran patients. We developed 3 algorithms that utilize the second eigenvalue of the graph Laplacian to summarize the complex graphical models of MCC by removing less significant edges. The first algorithm learns a sparse probabilistic graphical model of MCC interactions directly from the data. The second algorithm summarizes an existing probabilistic graphical model of MCC interactions when a supporting data set is available. The third algorithm, which is a variation of the second algorithm, summarizes the existing graphical model of MCC interactions with no supporting data. Finally, we examined the coappearance of the 100 most common terms in the literature of MCC to validate the performance of the proposed model.ResultsThe proposed summarization algorithms demonstrate considerable performance in extracting major connections among MCC without reducing the predictive accuracy of the resulting graphical models. For the model learned directly from the data, the area under the curve (AUC) performance for predicting TBI, PTSD, BaPa, SuAb, and Depr, respectively, during the next 4 years is as follows—year 2: 79.91%, 84.04%, 78.83%, 82.50%, and 81.47%; year 3: 76.23%, 80.61%, 73.51%, 79.84%, and 77.13%; year 4: 72.38%, 78.22%, 72.96%, 77.92%, and 72.65%; and year 5: 69.51%, 76.15%, 73.04%, 76.72%, and 69.99%, respectively. This demonstrates an overall 12.07% increase in the cumulative sum of AUC in comparison with the classic multilevel temporal Bayesian network.ConclusionsUsing graph summarization can improve the interpretability and the predictive power of the complex graphical models of MCC.
Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study
Authors: Kwon Soonil , Hong Joonki , Choi Eue-Keun , Lee Byunghwan , Baik Changhyun , Lee Euijae , Jeong Eui-Rim , Koo Bon-Kwon , Oh Seil , Yi Yung 
Published: March 2020

Background
Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF).

Objective
We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals.

Methods
Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR).

Results
In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR.

Conclusions
A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations.

Trial Registration
ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188
Electronic Data Capture Versus Conventional Data Collection Methods in Clinical Pain Studies: Systematic Review and Meta-Analysis
Authors: Jibb Lindsay A , Khan James S , Seth Puneet , Lalloo Chitra , Mulrooney Lauren , Nicholson Kathryn , Nowak Dominik A , Kaur Harneel , Chee-A-Tow Alyssandra , Foster Joel , Stinson Jennifer N 
Published: March 2020

Background
The most commonly used means to assess pain is by patient self-reported questionnaires. These questionnaires have traditionally been completed using paper-and-pencil, telephone, or in-person methods, which may limit the validity of the collected data. Electronic data capture methods represent a potential way to validly, reliably, and feasibly collect pain-related data from patients in both clinical and research settings.

Objective
The aim of this study was to conduct a systematic review and meta-analysis to compare electronic and conventional pain-related data collection methods with respect to pain score equivalence, data completeness, ease of use, efficiency, and acceptability between methods.

Methods
We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (EMBASE), and Cochrane Central Register of Controlled Trials (CENTRAL) from database inception until November 2019. We included all peer-reviewed studies that compared electronic (any modality) and conventional (paper-, telephone-, or in-person–based) data capture methods for patient-reported pain data on one of the following outcomes: pain score equivalence, data completeness, ease of use, efficiency, and acceptability. We used random effects models to combine score equivalence data across studies that reported correlations or measures of agreement between electronic and conventional pain assessment methods.

Results
A total of 53 unique studies were included in this systematic review, of which 21 were included in the meta-analysis. Overall, the pain scores reported electronically were congruent with those reported using conventional modalities, with the majority of studies (36/44, 82%) that reported on pain scores demonstrating this relationship. The weighted summary correlation coefficient of pain score equivalence from our meta-analysis was 0.92 (95% CI 0.88-0.95). Studies on data completeness, patient- or provider-reported ease of use, and efficiency generally indicated that electronic data capture methods were equivalent or superior to conventional methods. Most (19/23, 83%) studies that directly surveyed patients reported that the electronic format was the preferred data collection method.

Conclusions
Electronic pain-related data capture methods are comparable with conventional methods in terms of score equivalence, data completeness, ease, efficiency, and acceptability and, if the appropriate psychometric evaluations are in place, are a feasible means to collect pain data in clinical and research settings.
Use of Apps to Promote Childhood Vaccination: Systematic Review
Authors: de Cock Caroline , van Velthoven Michelle , Milne-Ives Madison , Mooney Mary , Meinert Edward 
Published: March 2020

Background
Vaccination is a critical step in reducing child mortality; however, vaccination rates have declined in many countries in recent years. This decrease has been associated with an increase in the outbreak of vaccine-preventable diseases. The potential for leveraging mobile platforms to promote vaccination coverage has been investigated in the development of numerous mobile apps. Although many are available for public use, there is little robust evaluation of these apps.

Objective
This systematic review aimed to assess the effectiveness of apps supporting childhood vaccinations in improving vaccination uptake, knowledge, and decision making as well as the usability and user perceptions of these apps.

Methods
PubMed, Excerpta Medica Database (EMBASE), Web of Science, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and Education Resources Information Center (ERIC) databases were systematically searched for studies published between 2008 and 2019 that evaluated childhood vaccination apps. Two authors screened and selected studies according to the inclusion and exclusion criteria. Data were extracted and analyzed, and the studies were assessed for risk of bias.

Results
A total of 28 studies evaluating 25 apps met the inclusion criteria and were included in this analysis. Overall, 9 studies assessed vaccination uptake, of which 4 reported significant benefits (P<.001 or P=.03) of the implementation of the app. Similarly, 4 studies indicated a significant (P≤.054) impact on knowledge and on vaccination decision making. Patient perceptions, usability, and acceptability were generally positive. The quality of the included studies was found to be moderate to poor, with many aspects of the methodology being unclear.

Conclusions
There is little evidence to support the use of childhood vaccination apps to improve vaccination uptake, knowledge, or decision making. Further research is required to understand the dichotomous effects of vaccination-related information provision and the evaluation of these apps in larger, more robust studies. The methodology of studies must be reported more comprehensively to accurately assess the effectiveness of childhood vaccination apps and the risk of bias of studies.

International Registered Report Identifier (IRRID)
RR2-10.2196/16929
Digital Divide in Perceived Benefits of Online Health Care and Social Welfare Services: National Cross-Sectional Survey Study
Authors: Heponiemi Tarja , Jormanainen Vesa , Leemann Lars , Manderbacka Kristiina , Aalto Anna-Mari , Hyppönen Hannele 
Published: March 2020

Background
The number of online services in health care is increasing rapidly in developed countries. Users are expected to take a more skilled and active role in taking care of their health and prevention of ill health. This induces risks that users (especially those who need the services the most) will drop out of digital services, resulting in a digital divide or exclusion. To ensure wide and equal use of online services, all users must experience them as beneficial.

Objective
This study aimed to examine associations of (1) demographics (age, gender, and degree of urbanization), (2) self-rated health, (3) socioeconomic position (education, experienced financial hardship, labor market position, and living alone), (4) social participation (voting, satisfaction with relationships, and keeping in touch with friends and family members), and (5) access, skills, and extent of use of information and communication technologies (ICT) with perceived benefits of online health care and social welfare services. Associations were examined separately for perceived health, economic, and collaboration benefits.

Methods
We used a large random sample representative of the Finnish population including 4495 (56.77% women) respondents aged between 20 and 97 years. Analyses of covariance were used to examine the associations of independent variables with perceived benefits.

Results
Access to online services, ICT skills, and extent of use were associated with all examined benefits of online services. ICT skills seemed to be the most important factor. Poor self-rated health was also consistently associated with lower levels of perceived benefits. Similarly, those who were keeping in touch with their friends and relatives at least once a week perceived online services more often beneficial in all the examined dimensions. Those who had experienced financial hardship perceived fewer health and economic benefits than others. Those who were satisfied with their relationships reported higher levels of health and collaboration benefits compared with their counterparts. Also age, education, and degree of urbanization had some statistically significant associations with benefits but they seemed to be at least partly explained by differences in access, skills, and extent of use of online services.

Conclusions
According to our results, providing health care services online has the potential to reinforce existing social and health inequalities. Our findings suggest that access to online services, skills to use them, and extent of use play crucial roles in perceiving them as beneficial. Moreover, there is a risk of digital exclusion among those who are socioeconomically disadvantaged, in poor health, or socially isolated. In times when health and social services are increasingly offered online, this digital divide may predispose people with high needs for services to exclusion from them.