The Experts below are selected from a list of 133860 Experts worldwide ranked by ideXlab platform

Hanu Tyagi - One of the best experts on this subject based on the ideXlab platform.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Social Science Research Network, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using Information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, or patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness is low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so in traditional training methods. ICT-based training interventions make it feasible to customize content to cater to providers’ preferences. Objective: This study aimed to discover preferences among providers over form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. A k-mean clustering was conducted to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Then, we compared the accuracy of the cluster-based prediction model with that based on demographic variables of providers (e.g., specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for the form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement when compared with targeting providers based on demographic variables.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Jmir mhealth and uhealth, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, and patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness of these programs is the low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so with traditional training Objective: This study aimed to discover preferences among providers on the form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. K-means clustering was used to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Subsequently, we compared the accuracy of the cluster-based prediction model with one based on demographic variables of providers (eg, specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement as compared to targeting providers based on demographic variables.

Sarang Deo - One of the best experts on this subject based on the ideXlab platform.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Social Science Research Network, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using Information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, or patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness is low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so in traditional training methods. ICT-based training interventions make it feasible to customize content to cater to providers’ preferences. Objective: This study aimed to discover preferences among providers over form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. A k-mean clustering was conducted to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Then, we compared the accuracy of the cluster-based prediction model with that based on demographic variables of providers (e.g., specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for the form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement when compared with targeting providers based on demographic variables.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Jmir mhealth and uhealth, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, and patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness of these programs is the low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so with traditional training Objective: This study aimed to discover preferences among providers on the form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. K-means clustering was used to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Subsequently, we compared the accuracy of the cluster-based prediction model with one based on demographic variables of providers (eg, specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement as compared to targeting providers based on demographic variables.

W J Vogel - One of the best experts on this subject based on the ideXlab platform.

  • cumulative fade distributions and frequency scaling techniques at 20 ghz from the advanced Communications Technology satellite and at 12 ghz from the digital satellite system
    Proceedings of the IEEE, 1997
    Co-Authors: J Goldhirsh, B H Musiani, W J Vogel
    Abstract:

    Cumulative fade distributions were derived from measured transmissions at 20 GHz emanating from the geostationary Advanced Communications Technology Satellite (ACTS) and at 12 GHz from the television broadcasting digital satellite system (DSS) over the one-year period September 1, 1995-August 31, 1996. The transmissions were acquired at two collocated receivers at the Applied Physics Laboratory of The Johns Hopkins University (central Maryland). Since both geostationary satellites are positioned within 1/spl deg/ from one another, the geometric pointing parameters at the receiver locations are approximately coincident (e.g., 38/spl deg/ elevation angle). The 20-GHz fades were noted to be two-four times larger than those measured at 12 GHz. Two frequency scaling techniques were employed for estimating the distribution at one frequency given a measurement at the other. The methods pertained to the frequency scaling formulation of the radioCommunications sector of the International TeleCommunications Union and the ratio of attenuations in terms of an equal probability rain rate along an effective path. The latter method gave agreement to within 1 dB when adjusted for antenna-wetting signal degradation.

Arnab Pal - One of the best experts on this subject based on the ideXlab platform.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Social Science Research Network, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using Information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, or patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness is low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so in traditional training methods. ICT-based training interventions make it feasible to customize content to cater to providers’ preferences. Objective: This study aimed to discover preferences among providers over form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. A k-mean clustering was conducted to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Then, we compared the accuracy of the cluster-based prediction model with that based on demographic variables of providers (e.g., specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for the form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement when compared with targeting providers based on demographic variables.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Jmir mhealth and uhealth, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, and patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness of these programs is the low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so with traditional training Objective: This study aimed to discover preferences among providers on the form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. K-means clustering was used to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Subsequently, we compared the accuracy of the cluster-based prediction model with one based on demographic variables of providers (eg, specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement as compared to targeting providers based on demographic variables.

Nishi Dixit - One of the best experts on this subject based on the ideXlab platform.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Social Science Research Network, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using Information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, or patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness is low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so in traditional training methods. ICT-based training interventions make it feasible to customize content to cater to providers’ preferences. Objective: This study aimed to discover preferences among providers over form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. A k-mean clustering was conducted to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Then, we compared the accuracy of the cluster-based prediction model with that based on demographic variables of providers (e.g., specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for the form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement when compared with targeting providers based on demographic variables.

  • leveraging providers preferences to customize instructional content in information and Communications Technology based training interventions retrospective analysis of a mobile phone based intervention in india
    Jmir mhealth and uhealth, 2020
    Co-Authors: Hanu Tyagi, Manisha Sabharwal, Nishi Dixit, Arnab Pal, Sarang Deo
    Abstract:

    Background: Many public health programs and interventions across the world increasingly rely on using information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, and patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness of these programs is the low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so with traditional training Objective: This study aimed to discover preferences among providers on the form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. K-means clustering was used to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Subsequently, we compared the accuracy of the cluster-based prediction model with one based on demographic variables of providers (eg, specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement as compared to targeting providers based on demographic variables.