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7 Research products, page 1 of 1

  • Canada
  • Research data
  • 2013-2022
  • Dataset
  • Canadian Institutes of Health Research
  • Rural Digital Europe

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  • Authors: 
    Okonofua, Friday; Yaya, Sanni; Ntoimo, Lorretta Favour; Igboin, Brian; Imongan, Wilson; Ogungbangbe, Julius;
    Publisher: ICPSR - Interuniversity Consortium for Political and Social Research
    Project: CIHR

    Nigeria is estimated to account for 19% of all estimated global maternal deaths with approximately 58,000 in 2015. The high number is partly due to the inadequate access of women to evidence-based skilled pregnancy care. The Federal Ministry of Health (FMoH) and all major health policy agencies in Nigeria have recognized the need for increased access to skilled obstetric care, especially in rural areas, as critical to reducing the high rate of maternal mortality. However, despite the fact that policymakers recognize that primary health care should play a key role in improving rural women's access to skilled pregnancy care, Primary Health Centres (PHCs) are often poorly utilized throughout the country. This project is a 5-year (2015-2020) implementation research conducted by the Women's Health and Action Research Centre (WHARC), Benin City, Nigeria in collaboration with the University of Ottawa (UOttawa), Canada and with funding from the International Development Research Centre (IDRC), Global Affairs Canada (GAC) and the Canadian Institute for Health Research (CIHR) under the Innovating for Maternal and Child Health in Africa (IMCHA) Initiative. The project's specific objectives are: 1) to identify the demand and supply factors responsible for the use and non-use of PHCs for pregnancy care in Esan South East and Etsako East LGAs of Edo State, Nigeria; 2) based on Objective 1, to derive and implement a set of multi-faceted community-led interventions to increase women's access to skilled pregnancy care offered in PHCs in Esan South East and Etsako East Local Government Areas (LGA); and 3) to evaluate the effectiveness of the interventions using both indicators of access to services, as well as maternal and fetal/newborn health outcomes in the intervention communities. The study was conducted in Esan South East and Etsako East Local Government Areas (LGAs) in Edo State in southern Nigeria. Both LGAs are located in the rural and riverine areas of the state, adjacent to River Niger, with Estako East in the northern part of the Edo State part of the river, while Esan South East is in the southern part. Edo State is one of Nigeria’s thirty-six states. Each state consists of LGAs, and LGAs consist of political/health Wards. The study was originally designed to be a randomized control trial (Yaya et al., 2018) but was changed to a quasi-experiment separate sample pretest and posttest design. The change was necessitated by the difficulty in achieving reliable randomization in the study communities. The study was conducted in three phases. At phase one, a baseline was conducted using a mixed-method approach to address objective 1. Based on the results of the baseline research, a set of intervention activities were designed and implemented simultaneously in phase 2 for two years. Phase three was the endline research which addressed the study objective 3. Ethical approval for the study was obtained from the National Health Research Ethics Committee (NHREC) of Nigeria – protocol number NHREC/01/01/2007 – 10/04/2017; and written informed consent was obtained from individual respondent/participant, except in the community conversations where the consent was verbal. The data we are sharing contain baseline and endline data. collected through a mixed-method approach to address the study objectives. The baseline data were collected between July 29 to August 16, 2017, using a mixed-method that comprises a household survey, exit interview, PHC site assessment survey, community conversation, focus group discussion, and key informant interview. The endline data were collected between June 24 and July 6, 2020, using a household survey. All the data collection instruments were pretested and the data were collected by trained data collectors. Response Rates: The sample size for the baseline and end household survey was 1,318, to adjust for non-response, 10% was added to derive a total of 1,450. At baseline, 1408 responded, and at endline 1,411 responded. Based on replacement of non-response, the total number expected were covered during the two surveys bringing the response rate to be 100% Household survey: Multistage, systematic, random sampling design;Exit Interview: All eligible women were interviewed;Site Assessment survey: Random sampling;Qualitative data: Purposive and convenient sampling Ever married women age 15-45 years oldPrimary Health Centres. Smallest Geographic Unit: Local Government Area computer-assisted personal interview (CAPI); face-to-face interview;

  • Open Access
    Authors: 
    Naud, Daniel; Généreux, Mélissa; Jean-François Bruneau; Alauzet, Aline; Levasseur, Mélanie;
    Publisher: figshare
    Project: SSHRC , CIHR

    Gender distribution by population size group. (XLSX 16 kb)

  • Open Access
    Authors: 
    Audate, Pierre; Fernandez, Melissa; GenevièVe Cloutier; Lebel, Alexandre;
    Publisher: figshare
    Project: CIHR

    Quality Appraisal for Quantitative studies and mixed methods studies. (XLSX 21 kb)

  • Open Access
    Authors: 
    Audate, Pierre; Fernandez, Melissa; GenevièVe Cloutier; Lebel, Alexandre;
    Publisher: figshare
    Project: CIHR

    Quality appraisal for qualitative and mixed methods studies. (XLSX 19 kb)

  • Open Access
    Authors: 
    Audate, Pierre; Fernandez, Melissa; GenevièVe Cloutier; Lebel, Alexandre;
    Publisher: figshare
    Project: CIHR

    Description of included studies. (XLSX 53 kb)

  • Open Access
    Authors: 
    Tavakolan, Mojgan; Frehlick, Zack; Yong, Xinyi; Menon, Carlo;
    Publisher: Zenodo
    Project: NSERC , CIHR

    P01Se01EEG data were recorded from participant 1(session 1). For further details, please see the associated README file.P01Session01.zipP01Se02EEG data were recorded from participant 1(session 2). For further details, please see the associated README file.P01Session02.zipP01Se03EEG data were recorded from participant 1(session 3). For further details, please see the associated README file.P01Session03.zipP01Se04EEG data were recorded from participant 1(session 4). For further details, please see the associated README file.P01Session04.zipP02Se01EEG data were recorded from participant 2(session 1). For further details, please see the associated README file.P02Session01.zipP03Se01EEG data were recorded from participant 3(session 1). For further details, please see the associated README file.P03Session01.zipP04Se01EEG data were recorded from participant 4(session 1). For further details, please see the associated README file.P05Se01EEG data were recorded from participant 5(session 1). For further details, please see the associated README file.P06Se01EEG data were recorded from participant 6(session 1). For further details, please see the associated README file.P07Se01EEG data were recorded from participant 7(session 1). For further details, please see the associated README file.P08Se01EEG data were recorded from participant 8(session 1). For further details, please see the associated README file.P09Se01EEG data were recorded from participant 9(session 1). For further details, please see the associated README file.P10Se01EEG data were recorded from participant 10(session 1). For further details, please see the associated README file.P11Se01EEG data were recorded from participant 11(session 1). For further details, please see the associated README file.P12Se01EEG data were recorded from participant 12(session 1). For further details, please see the associated README file.P02Se02EEG data were recorded from participant 2(session 2). For further details, please see the associated README file.P03Se02EEG data were recorded from participant 3(session 2). For further details, please see the associated README file.P04Se02EEG data were recorded from participant 4(session 2). For further details, please see the associated README file.P05Se02EEG data were recorded from participant 5(session 2). For further details, please see the associated README file.P06Se02EEG data were recorded from participant 6(session 2). For further details, please see the associated README file.P07Se02EEG data were recorded from participant 7(session 2). For further details, please see the associated README file.P08Se02EEG data were recorded from participant 8(session 2). For further details, please see the associated README file.P09Se02EEG data were recorded from participant 9(session 2). For further details, please see the associated README file.P10Se02EEG data were recorded from participant 10(session 2). For further details, please see the associated README file.P11Se02EEG data were recorded from participant 11(session 2). For further details, please see the associated README file.P12Se02EEG data were recorded from participant 12(session 2). For further details, please see the associated README file.P02Se03EEG data were recorded from participant 2(session 3). For further details, please see the associated README file.P03Se03EEG data were recorded from participant 3(session 3). For further details, please see the associated README file.P04Se03EEG data were recorded from participant 4(session 3). For further details, please see the associated README file.P05Se03EEG data were recorded from participant 5(session 3). For further details, please see the associated README file.P06Se03EEG data were recorded from participant 6(session 3). For further details, please see the associated README file.P07Se03EEG data were recorded from participant 7(session 3). For further details, please see the associated README file.P08Se03EEG data were recorded from participant 8(session 3). For further details, please see the associated README file.P09Se03EEG data were recorded from participant 9(session 3). For further details, please see the associated README file.P10Se03EEG data were recorded from participant 10(session 3). For further details, please see the associated README file.P11Se03EEG data were recorded from participant 11(session 3). For further details, please see the associated README file.P12Se03EEG data were recorded from participant 12(session 3). For further details, please see the associated README file.P02Se04EEG data were recorded from participant 2(session 4). For further details, please see the associated README file.P03Se04EEG data were recorded from participant 3(session 4). For further details, please see the associated README file.P04Se04EEG data were recorded from participant 4(session 4). For further details, please see the associated README file.P05Se04EEG data were recorded from participant 5(session 4). For further details, please see the associated README file.P06Se04EEG data were recorded from participant 6(session 4). For further details, please see the associated README file.P07Se04EEG data were recorded from participant 7(session 4). For further details, please see the associated README file.P08Se04EEG data were recorded from participant 8(session 4). For further details, please see the associated README file.P09Se04EEG data were recorded from participant 9(session 4). For further details, please see the associated README file.P10Se04EEG data were recorded from participant 10(session 4). For further details, please see the associated README file.P11Se04EEG data were recorded from participant 11(session 4). For further details, please see the associated README file.P12Se04EEG data were recorded from participant 12(session 4). For further details, please see the associated README file. Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.

  • Open Access
    Authors: 
    Naud, Daniel; Généreux, Mélissa; Jean-François Bruneau; Alauzet, Aline; Levasseur, Mélanie;
    Publisher: figshare
    Project: CIHR , SSHRC

    Gender distribution by region. (XLSX 17 kb)

Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
7 Research products, page 1 of 1
  • Authors: 
    Okonofua, Friday; Yaya, Sanni; Ntoimo, Lorretta Favour; Igboin, Brian; Imongan, Wilson; Ogungbangbe, Julius;
    Publisher: ICPSR - Interuniversity Consortium for Political and Social Research
    Project: CIHR

    Nigeria is estimated to account for 19% of all estimated global maternal deaths with approximately 58,000 in 2015. The high number is partly due to the inadequate access of women to evidence-based skilled pregnancy care. The Federal Ministry of Health (FMoH) and all major health policy agencies in Nigeria have recognized the need for increased access to skilled obstetric care, especially in rural areas, as critical to reducing the high rate of maternal mortality. However, despite the fact that policymakers recognize that primary health care should play a key role in improving rural women's access to skilled pregnancy care, Primary Health Centres (PHCs) are often poorly utilized throughout the country. This project is a 5-year (2015-2020) implementation research conducted by the Women's Health and Action Research Centre (WHARC), Benin City, Nigeria in collaboration with the University of Ottawa (UOttawa), Canada and with funding from the International Development Research Centre (IDRC), Global Affairs Canada (GAC) and the Canadian Institute for Health Research (CIHR) under the Innovating for Maternal and Child Health in Africa (IMCHA) Initiative. The project's specific objectives are: 1) to identify the demand and supply factors responsible for the use and non-use of PHCs for pregnancy care in Esan South East and Etsako East LGAs of Edo State, Nigeria; 2) based on Objective 1, to derive and implement a set of multi-faceted community-led interventions to increase women's access to skilled pregnancy care offered in PHCs in Esan South East and Etsako East Local Government Areas (LGA); and 3) to evaluate the effectiveness of the interventions using both indicators of access to services, as well as maternal and fetal/newborn health outcomes in the intervention communities. The study was conducted in Esan South East and Etsako East Local Government Areas (LGAs) in Edo State in southern Nigeria. Both LGAs are located in the rural and riverine areas of the state, adjacent to River Niger, with Estako East in the northern part of the Edo State part of the river, while Esan South East is in the southern part. Edo State is one of Nigeria’s thirty-six states. Each state consists of LGAs, and LGAs consist of political/health Wards. The study was originally designed to be a randomized control trial (Yaya et al., 2018) but was changed to a quasi-experiment separate sample pretest and posttest design. The change was necessitated by the difficulty in achieving reliable randomization in the study communities. The study was conducted in three phases. At phase one, a baseline was conducted using a mixed-method approach to address objective 1. Based on the results of the baseline research, a set of intervention activities were designed and implemented simultaneously in phase 2 for two years. Phase three was the endline research which addressed the study objective 3. Ethical approval for the study was obtained from the National Health Research Ethics Committee (NHREC) of Nigeria – protocol number NHREC/01/01/2007 – 10/04/2017; and written informed consent was obtained from individual respondent/participant, except in the community conversations where the consent was verbal. The data we are sharing contain baseline and endline data. collected through a mixed-method approach to address the study objectives. The baseline data were collected between July 29 to August 16, 2017, using a mixed-method that comprises a household survey, exit interview, PHC site assessment survey, community conversation, focus group discussion, and key informant interview. The endline data were collected between June 24 and July 6, 2020, using a household survey. All the data collection instruments were pretested and the data were collected by trained data collectors. Response Rates: The sample size for the baseline and end household survey was 1,318, to adjust for non-response, 10% was added to derive a total of 1,450. At baseline, 1408 responded, and at endline 1,411 responded. Based on replacement of non-response, the total number expected were covered during the two surveys bringing the response rate to be 100% Household survey: Multistage, systematic, random sampling design;Exit Interview: All eligible women were interviewed;Site Assessment survey: Random sampling;Qualitative data: Purposive and convenient sampling Ever married women age 15-45 years oldPrimary Health Centres. Smallest Geographic Unit: Local Government Area computer-assisted personal interview (CAPI); face-to-face interview;

  • Open Access
    Authors: 
    Naud, Daniel; Généreux, Mélissa; Jean-François Bruneau; Alauzet, Aline; Levasseur, Mélanie;
    Publisher: figshare
    Project: SSHRC , CIHR

    Gender distribution by population size group. (XLSX 16 kb)

  • Open Access
    Authors: 
    Audate, Pierre; Fernandez, Melissa; GenevièVe Cloutier; Lebel, Alexandre;
    Publisher: figshare
    Project: CIHR

    Quality Appraisal for Quantitative studies and mixed methods studies. (XLSX 21 kb)

  • Open Access
    Authors: 
    Audate, Pierre; Fernandez, Melissa; GenevièVe Cloutier; Lebel, Alexandre;
    Publisher: figshare
    Project: CIHR

    Quality appraisal for qualitative and mixed methods studies. (XLSX 19 kb)

  • Open Access
    Authors: 
    Audate, Pierre; Fernandez, Melissa; GenevièVe Cloutier; Lebel, Alexandre;
    Publisher: figshare
    Project: CIHR

    Description of included studies. (XLSX 53 kb)

  • Open Access
    Authors: 
    Tavakolan, Mojgan; Frehlick, Zack; Yong, Xinyi; Menon, Carlo;
    Publisher: Zenodo
    Project: NSERC , CIHR

    P01Se01EEG data were recorded from participant 1(session 1). For further details, please see the associated README file.P01Session01.zipP01Se02EEG data were recorded from participant 1(session 2). For further details, please see the associated README file.P01Session02.zipP01Se03EEG data were recorded from participant 1(session 3). For further details, please see the associated README file.P01Session03.zipP01Se04EEG data were recorded from participant 1(session 4). For further details, please see the associated README file.P01Session04.zipP02Se01EEG data were recorded from participant 2(session 1). For further details, please see the associated README file.P02Session01.zipP03Se01EEG data were recorded from participant 3(session 1). For further details, please see the associated README file.P03Session01.zipP04Se01EEG data were recorded from participant 4(session 1). For further details, please see the associated README file.P05Se01EEG data were recorded from participant 5(session 1). For further details, please see the associated README file.P06Se01EEG data were recorded from participant 6(session 1). For further details, please see the associated README file.P07Se01EEG data were recorded from participant 7(session 1). For further details, please see the associated README file.P08Se01EEG data were recorded from participant 8(session 1). For further details, please see the associated README file.P09Se01EEG data were recorded from participant 9(session 1). For further details, please see the associated README file.P10Se01EEG data were recorded from participant 10(session 1). For further details, please see the associated README file.P11Se01EEG data were recorded from participant 11(session 1). For further details, please see the associated README file.P12Se01EEG data were recorded from participant 12(session 1). For further details, please see the associated README file.P02Se02EEG data were recorded from participant 2(session 2). For further details, please see the associated README file.P03Se02EEG data were recorded from participant 3(session 2). For further details, please see the associated README file.P04Se02EEG data were recorded from participant 4(session 2). For further details, please see the associated README file.P05Se02EEG data were recorded from participant 5(session 2). For further details, please see the associated README file.P06Se02EEG data were recorded from participant 6(session 2). For further details, please see the associated README file.P07Se02EEG data were recorded from participant 7(session 2). For further details, please see the associated README file.P08Se02EEG data were recorded from participant 8(session 2). For further details, please see the associated README file.P09Se02EEG data were recorded from participant 9(session 2). For further details, please see the associated README file.P10Se02EEG data were recorded from participant 10(session 2). For further details, please see the associated README file.P11Se02EEG data were recorded from participant 11(session 2). For further details, please see the associated README file.P12Se02EEG data were recorded from participant 12(session 2). For further details, please see the associated README file.P02Se03EEG data were recorded from participant 2(session 3). For further details, please see the associated README file.P03Se03EEG data were recorded from participant 3(session 3). For further details, please see the associated README file.P04Se03EEG data were recorded from participant 4(session 3). For further details, please see the associated README file.P05Se03EEG data were recorded from participant 5(session 3). For further details, please see the associated README file.P06Se03EEG data were recorded from participant 6(session 3). For further details, please see the associated README file.P07Se03EEG data were recorded from participant 7(session 3). For further details, please see the associated README file.P08Se03EEG data were recorded from participant 8(session 3). For further details, please see the associated README file.P09Se03EEG data were recorded from participant 9(session 3). For further details, please see the associated README file.P10Se03EEG data were recorded from participant 10(session 3). For further details, please see the associated README file.P11Se03EEG data were recorded from participant 11(session 3). For further details, please see the associated README file.P12Se03EEG data were recorded from participant 12(session 3). For further details, please see the associated README file.P02Se04EEG data were recorded from participant 2(session 4). For further details, please see the associated README file.P03Se04EEG data were recorded from participant 3(session 4). For further details, please see the associated README file.P04Se04EEG data were recorded from participant 4(session 4). For further details, please see the associated README file.P05Se04EEG data were recorded from participant 5(session 4). For further details, please see the associated README file.P06Se04EEG data were recorded from participant 6(session 4). For further details, please see the associated README file.P07Se04EEG data were recorded from participant 7(session 4). For further details, please see the associated README file.P08Se04EEG data were recorded from participant 8(session 4). For further details, please see the associated README file.P09Se04EEG data were recorded from participant 9(session 4). For further details, please see the associated README file.P10Se04EEG data were recorded from participant 10(session 4). For further details, please see the associated README file.P11Se04EEG data were recorded from participant 11(session 4). For further details, please see the associated README file.P12Se04EEG data were recorded from participant 12(session 4). For further details, please see the associated README file. Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.

  • Open Access
    Authors: 
    Naud, Daniel; Généreux, Mélissa; Jean-François Bruneau; Alauzet, Aline; Levasseur, Mélanie;
    Publisher: figshare
    Project: CIHR , SSHRC

    Gender distribution by region. (XLSX 17 kb)