cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20376278
title: Primary care practice characteristics associated with team functioning in primary care settings in Canada: A practice-based cross-sectional survey

description: Team-based care is recognized as a foundational building block of high-performing primary care. The purpose of this study was to identify primary care practice characteristics associated with team functioning and examine whether there is relationship between team composition or size and team functioning. We sought to answer the following research questions: (1) are primary care practice characteristics associated with team functioning; and (2) does team composition or size influence team functioning. This cross-sectional correlational study was conducted in Fraser East, British Columbia, Eastern Ontario Health Unit, Ontario and Central Zone, Nova Scotia in Canada. Data were collected from primary care practices using an organization survey and the Team Climate Inventory (TCI) as a measure team functioning. The independent variables of interest were: physicians’ payment model, internal clinic meetings to discuss clinical issues, care coordination through informal and ad hoc exchange, care coordination through electronic medical records and sharing clinic mission, values and objectives among health professionals. Potentially confounding variables were as follows: team size, composition, and practice panel size. A total of 63 practices were included in these analyses. The overall mean score of team climate was 73 (SD: 10.75) out of 100. Regression analyses showed that care coordination through human interaction and sharing the practice’s mission, values, and objectives among health professionals were positively associated with higher functioning teams. Care coordination through electronic medical records and larger team size were negatively associated with team climate. This study provides baseline data on what practice characteristics are associated with highly functioning teams in Canada.
DOI: 10.6084/m9.figshare.20702924
title: The evidence base for interprofessional education within health professions education: A protocol for an update review

description: Interprofessional education (IPE) interventions aiming to promote collaborative competence and improve the delivery of health and social care processes and outcomes continue to evolve. This paper reports on a protocol for an update review that we will conduct to identify and describe how the IPE evidence base has evolved in the last 7 years. We will identify literature through a systematic search of the following electronic databases: Medline, Embase, CINAHL, Education Source, ERIC, and BEI. We will consider all IPE interventions delivered to health professions students and accredited professionals. Peer-reviewed empirical research studies published in any language from June 2014 onwards will be eligible for inclusion. The outcomes of interest are changes in the reaction, attitudes/perceptions, knowledge/skills acquisition, behaviors, organizational practice, and/or benefits to patients. We will perform each task of screening, critical appraisal, data abstraction, and synthesis using at least two members of the review team. The review will enable an update and comprehensive understanding of the IPE evidence base to inform future IPE developments, delivery and evaluation across education and clinical settings.
DOI: 10.6084/m9.figshare.20051768
title: Preliminary evidence for a Tool to Observe the Construction of Knowledge in Interprofessional teams (TOCK–IP)

description: Collaborative knowledge construction (KC) is an important process in interprofessional learning and a logical assessment target. A tool supporting the formative evaluation of KC behaviors ideally would be: 1) applicable to interprofessional teams of learners in clinical contexts; 2) informed by contemporary learning frameworks; 3) feasible and useful. No existing assessment tool meets these criteria. This paper describes the development and preliminary validity evidence for a Tool for Observing Construction of Knowledge in Interprofessional teams (TOCK-IP). Following literature review and needs assessment, the TOCK-IP was drafted based upon Gunawardena’s five-phase KC model. Educational expert review established content validity. Response process and internal structure validity, feasibility, and utility were assessed through step-wise evaluation. Faculty raters applied the tool to four videos of simulated interactions between health professions learners. Faculty ratings were compared to expert consensus ratings. Thematic analysis of post-rating survey and debrief allowed assessment of feasibility and utility. Across videos, faculty raters’ agreement was fair (n = 25; Fleiss’ kappa = 0.40, &lt;0.001). Excellent agreement (95%) was found for raters’ scores compared to consensus rating. Faculty supported tool feasibility and utility. The TOCK-IP meets the three criteria for evaluating team-level KC and offers a progression roadmap to help learners move toward collaborative learning.
DOI: 10.6084/m9.figshare.20051768.v1
title: Preliminary evidence for a Tool to Observe the Construction of Knowledge in Interprofessional teams (TOCK–IP)

description: Collaborative knowledge construction (KC) is an important process in interprofessional learning and a logical assessment target. A tool supporting the formative evaluation of KC behaviors ideally would be: 1) applicable to interprofessional teams of learners in clinical contexts; 2) informed by contemporary learning frameworks; 3) feasible and useful. No existing assessment tool meets these criteria. This paper describes the development and preliminary validity evidence for a Tool for Observing Construction of Knowledge in Interprofessional teams (TOCK-IP). Following literature review and needs assessment, the TOCK-IP was drafted based upon Gunawardena’s five-phase KC model. Educational expert review established content validity. Response process and internal structure validity, feasibility, and utility were assessed through step-wise evaluation. Faculty raters applied the tool to four videos of simulated interactions between health professions learners. Faculty ratings were compared to expert consensus ratings. Thematic analysis of post-rating survey and debrief allowed assessment of feasibility and utility. Across videos, faculty raters’ agreement was fair (n = 25; Fleiss’ kappa = 0.40, &lt;0.001). Excellent agreement (95%) was found for raters’ scores compared to consensus rating. Faculty supported tool feasibility and utility. The TOCK-IP meets the three criteria for evaluating team-level KC and offers a progression roadmap to help learners move toward collaborative learning.
DOI: 10.6084/m9.figshare.20376267.v1
title: Factors associated with interprofessional competencies among healthcare professionals in Japan

description: We aimed to explore factors associated with interprofessional competencies among healthcare professionals in Japan. From June to October 2020, we conducted a cross-sectional survey via a validated self-administered web-based questionnaire using the Japanese version of the Self-assessment Scale of Interprofessional Competency (JASSIC). We recruited participants from an e-mail list. The questionnaire asked about JASSIC, basic demographic information, whether they had undertaken pre- and post-licensure interprofessional education (IPE), and administrative experience; as well as an organizational climate scale, including “Plan, Do, See” factor for management (PDS factor), and the “Do” factor in a leader-centered direction for people who work unwillingly. Factors associated with the total JASSIC score as interprofessional competencies were determined using multiple regression analysis. We analyzed data from 560 participants with an average age of 41.0 years, comprising 132 nurses, 127 doctors, and 120 social workers. The median of the total JASSIC score was 72/90 (range: 66–78). On multiple regression analysis, total JASSIC score was significantly associated with age, PDS factor, administrative experience, pre-licensure IPE, and pos-licensure IPE. These findings emphasize the importance of pre- and post-licensure IPE, and administrative experience for improving interprofessional competencies in Japan.
DOI: 10.34740/kaggle/dsv/5546887
title: Russian biggest government procurement contracts
DOI: 10.34740/kaggle/ds/3194763
title: Russian biggest government procurement contracts
DOI: 10.5281/zenodo.6359399
title: Pain Interventions in Dementia - Pain events dataset

description: This is data collected for a quasi-experimental nurse-led intervention trial based on a convenience sample of three nursing homes. It was collected in the Swiss Canton of Zurich and Thurgau and serves to examine the effects on dementia patients, the healthcare institution, and the qualification level of the healthcare workers using an event analysis and a multilevel analysis. Healthcare workers have been individually trained on how to assess, intervene and evaluate acute and chronic pain with BESD and/or VAS. There are three data-monitoring cycles (T0, T1, T2) and two intervention cycles (I1, I2) with a total study duration of 425 days. The raw data has been cryptographically anonymized using an SSL stream and further de-identification techniques. Also see: 10.1186/s12904-017-0200-5
DOI: 10.5281/zenodo.6359400
title: Pain Interventions in Dementia - Pain events dataset

description: This is data collected for a quasi-experimental nurse-led intervention trial based on a convenience sample of three nursing homes. It was collected in the Swiss Canton of Zurich and Thurgau and serves to examine the effects on dementia patients, the healthcare institution, and the qualification level of the healthcare workers using an event analysis and a multilevel analysis. Healthcare workers have been individually trained on how to assess, intervene and evaluate acute and chronic pain with BESD and/or VAS. There are three data-monitoring cycles (T0, T1, T2) and two intervention cycles (I1, I2) with a total study duration of 425 days. The raw data has been cryptographically anonymized using an SSL stream and further de-identification techniques. Also see: 10.1186/s12904-017-0200-5
DOI: 10.5281/zenodo.7746395
title: Data Set: In-session dropout prediction model

description: In-session dropout prediction model This project describes an in-session prediction model that predicts student early dropout from online learning exercises.<br> Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in<br> the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates,<br> school homework and assignments are supposed to be completed by all learners. In the pandemic, online<br> learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to<br> transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting<br> learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the<br> online language learning platform orthografietrainer.net. We calculated time-progressive machine learning<br> models that predict dropout after each step (completed sentence) in the assignment using learning process<br> data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention<br> strategy that distinguishes between motivational and subject-specific interventions. <br> A random state is not set, thus, results might differ marginally. Whole project described in: <br> N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart<br> Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios<br> Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,<br> SciTePress, 2022, ISBN 978-989-758-562-3
description: {"references": ["N. Rzepka, K. Simbeck, H.-G. M\u00fcller, and N. Pinkwart Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, SciTePress, 2022, ISBN 978-989-758-562-3"]}
DOI: 10.5281/zenodo.7746394
title: Data Set: In-session dropout prediction model

description: In-session dropout prediction model This project describes an in-session prediction model that predicts student early dropout from online learning exercises.<br> Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in<br> the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates,<br> school homework and assignments are supposed to be completed by all learners. In the pandemic, online<br> learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to<br> transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting<br> learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the<br> online language learning platform orthografietrainer.net. We calculated time-progressive machine learning<br> models that predict dropout after each step (completed sentence) in the assignment using learning process<br> data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention<br> strategy that distinguishes between motivational and subject-specific interventions. <br> A random state is not set, thus, results might differ marginally. Whole project described in: <br> N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart<br> Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios<br> Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,<br> SciTePress, 2022, ISBN 978-989-758-562-3
description: {"references": ["N. Rzepka, K. Simbeck, H.-G. M\u00fcller, and N. Pinkwart Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, SciTePress, 2022, ISBN 978-989-758-562-3"]}
DOI: 10.5281/zenodo.7755362
title: Data Set: Solution Probability in Online Learning Environments

description: <pre>Solution Probability Model and Fairness Evaluation This in-session prediction model seeks to predict the users’ performance on the Orthografietrainer.net platform. The target variable is binary and predicts if the user will do the following sentence correctly or not. For fairness evaluations the best models (MLP and DTE), and the worst model (SVM) are considered. A random state is not set, thus, results might differ marginally. A detailed description of the solution probability model and the fairness evaluation can be found here: tba</pre>
DOI: 10.5281/zenodo.7755363
title: Data Set: Solution Probability in Online Learning Environments

description: <pre>Solution Probability Model and Fairness Evaluation This in-session prediction model seeks to predict the users’ performance on the Orthografietrainer.net platform. The target variable is binary and predicts if the user will do the following sentence correctly or not. For fairness evaluations the best models (MLP and DTE), and the worst model (SVM) are considered. A random state is not set, thus, results might differ marginally. A detailed description of the solution probability model and the fairness evaluation can be found here: tba</pre>
DOI: 10.5281/zenodo.7858567
title: Tunable directional photon scattering from a pair of superconducting qubits

description: This dataset comprises all data shown in the figures of the submitted article "Tunable directional photon scattering from a pair of superconducting qubits" at arXiv:2205.03293. Additional raw data are available from the corresponding author on reasonable request.
DOI: 10.5281/zenodo.7858566
title: Tunable directional photon scattering from a pair of superconducting qubits

description: This dataset comprises all data shown in the figures of the submitted article "Tunable directional photon scattering from a pair of superconducting qubits" at arXiv:2205.03293. Additional raw data are available from the corresponding author on reasonable request.
DOI: 10.5281/zenodo.7755493
title: Data Set of an online controlled experiment to study adaptive learning

description: Online-controlled experiment evaluation - Data Set Digital learning platforms are more and more used in blended classroom scenarios in Germany. However, as learning processes are different among students, adaptive learning platforms can offer personalized learning, e.g. by individual feedback and corrections, task sequencing, or recommendations. As digital learning platforms are already used in classroom settings, we propose the transformation of these plat-forms into adaptive learning environments. To measure the effectiveness and improvements achieved through the adaptions an online-controlled experiment design is created. In our experiment, we therefore investigate the effectiveness of different inter-ventions on a large user group in a four-month online-controlled experiment. For this purpose, the highly frequented German learning platform Orthografietrainer.net was transformed into an adaptive learning platform and users were randomly assigned to different interventions. The experimental design is published here: N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart An Online Controlled Experiment Design to Support the Transformation of Digital Learning towards Adaptive Learning Platforms Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,, SciTePress, 2022, ISBN 978-989-758-562-3 The architectural concept is published here: Rzepka, N., Simbeck, K., Müller, H.-G. & Pinkwart, N., (2022). Adaptive Learning as a Service – A concept to extend digital learning platforms?. In: Henning, P. A., Striewe, M.-0. 0. & Wölfel, M.-0. 0. (Hrsg.), 20. Fachtagung Bildungstechnologien (DELFI). Bonn: Gesellschaft für Informatik e.V.. (S. 237-238). DOI: 10.18420/delfi2022-049 The findings of this experiment are published here: tba The code to this evaluation can be found on Zenodo: 10.5281/zenodo.7755546
DOI: 10.5281/zenodo.7755494
title: Data Set of an online controlled experiment to study adaptive learning

description: Online-controlled experiment evaluation - Data Set Digital learning platforms are more and more used in blended classroom scenarios in Germany. However, as learning processes are different among students, adaptive learning platforms can offer personalized learning, e.g. by individual feedback and corrections, task sequencing, or recommendations. As digital learning platforms are already used in classroom settings, we propose the transformation of these plat-forms into adaptive learning environments. To measure the effectiveness and improvements achieved through the adaptions an online-controlled experiment design is created. In our experiment, we therefore investigate the effectiveness of different inter-ventions on a large user group in a four-month online-controlled experiment. For this purpose, the highly frequented German learning platform Orthografietrainer.net was transformed into an adaptive learning platform and users were randomly assigned to different interventions. The experimental design is published here: N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart An Online Controlled Experiment Design to Support the Transformation of Digital Learning towards Adaptive Learning Platforms Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,, SciTePress, 2022, ISBN 978-989-758-562-3 The architectural concept is published here: Rzepka, N., Simbeck, K., Müller, H.-G. & Pinkwart, N., (2022). Adaptive Learning as a Service – A concept to extend digital learning platforms?. In: Henning, P. A., Striewe, M.-0. 0. & Wölfel, M.-0. 0. (Hrsg.), 20. Fachtagung Bildungstechnologien (DELFI). Bonn: Gesellschaft für Informatik e.V.. (S. 237-238). DOI: 10.18420/delfi2022-049 The findings of this experiment are published here: tba The code to this evaluation can be found on Zenodo: 10.5281/zenodo.7755546
DOI: 10.5281/zenodo.7874696
title: Python code for "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"

description: The file contains the Python code used to run the agent-based simulation of the selection-mutation model presented in "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"
DOI: 10.5281/zenodo.7874695
title: Python code for "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"

description: The file contains the Python code used to run the agent-based simulation of the selection-mutation model presented in "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"
DOI: 10.5517/ccdc.csd.cc2f36qh
title: CCDC 2237416: Experimental Crystal Structure Determination

description: Related Article: Yunpeng Lou, Rui Shi, Li Yu, Ting Jiang, Haoquan Zhang, Lifeng Zhang, Yongxu Hu, Deyang Ji, Yajing Sun, Jie Li, Liqiang Li, Wenping Hu|2023|RSC Advances|13|11706|doi:10.1039/D3RA00600J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f3wyd
title: CCDC 2238074: Experimental Crystal Structure Determination

description: Related Article: Narayan Ch. Jana, Marko Jagodič, Paula Brandão, Moumita Patra, Radovan Herchel, Zvonko Jagličić, Anangamohan Panja|2023|RSC Advances|13|11311|doi:10.1039/D3RA00737E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f3wzf
title: CCDC 2238075: Experimental Crystal Structure Determination

description: Related Article: Narayan Ch. Jana, Marko Jagodič, Paula Brandão, Moumita Patra, Radovan Herchel, Zvonko Jagličić, Anangamohan Panja|2023|RSC Advances|13|11311|doi:10.1039/D3RA00737E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f3x0h
title: CCDC 2238076: Experimental Crystal Structure Determination

description: Related Article: Narayan Ch. Jana, Marko Jagodič, Paula Brandão, Moumita Patra, Radovan Herchel, Zvonko Jagličić, Anangamohan Panja|2023|RSC Advances|13|11311|doi:10.1039/D3RA00737E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.