Integrated model for women empowerment in rural India

Sharma, Eliza; Das, Subhankar

doi:10.1002/jid.3539

Cited by 36 publications

(6 citation statements)

References 40 publications

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“…These services can provide information about the safety and efficacy of vaccines and address common concerns and misconceptions. Additionally, they can help individuals locate vaccination sites and schedule appointments [ 101 ].…”

Section: Discussionmentioning

confidence: 99%

Scale Development for COVID-19 Vaccine Hesitancy by Integration of Socio-Demographic and Psychological Factors

et al. 2023

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Vaccination is the most cost-effective way to maintain population health. However, it can only be effective if widespread acceptance is held. The efficacy of COVID-19 vaccines depends on their favor. When countries start to vaccinate their citizens, there is a certain level of skepticism about the effectiveness of the vaccines. The hesitancy study on vaccines has gained momentum following the pandemic. However, few studies have examined the link between the psychological and sociodemographic factors influencing the fit. This paper proposes integrating the concepts of the information systems success and stimulus–organism–response into a cognitive fit theory framework to explore the integration of psychological and sociodemographic factors in the receivers’ reactions (n = 1510). This study analyses the factors that influence the hesitancy of vaccines and the public’s refusal in Asia and Europe. Receivers’ reactions were assessed to various stimuli and we explored the link between psychological and sociodemographic elements and the concept of fit. Two surveys were conducted following the scale development of Mackenzie. The first was to develop the fit scale, while the second was to validate the fit scale. The results of the second survey were analyzed using structural equation modelling. The results indicate that the scale’s fit development is valid and reliable. The quality of the vaccine information, the psychological characteristics of the vaccine system, and vaccine receivers’ satisfaction are also beneficial factors for emotional and cognitive fit. Maintaining the vaccines’ quality and efficiency can help improve the fit between sociodemographic and psychological characteristics. It can also enhance receivers’ satisfaction and encourage continued vaccine administration. This study is regarded as one of the first to examine and develop an emotional and cognitive fit scale for practitioners and researchers.

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Section: Discussionmentioning

confidence: 99%

Scale Development for COVID-19 Vaccine Hesitancy by Integration of Socio-Demographic and Psychological Factors

et al. 2023

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“…• Peer-reviewed paper (Biswas and Kabir, 2004;Santillán et al, 2004;Varghese, 2011;Alkire et al, 2013;Bhattacharya and Banerjee, 2013;Upadhyay et al, 2014;Yount et al, 2016;Rafiey et al, 2018;Galiè et al, 2019;Hinson et al, 2019;Malapit et al, 2019;Narayanan et al, 2019;Sharaunga et al, 2019;Moreau et al, 2020;Dickin et al, 2021;Moubarak et al, 2021;Sharma and Das, 2021;Saha and Narayanan, 2022;Sinharoy et al, 2022) • Report (Lombardini et al, 2017;The Hunger Project, 2017;MEASURE Evaluation, 2020) • Other (e.g., discussion paper, working paper) (Williams, 2005;Malapit et al, 2017) Empowerment areas…”

Section: Characteristics Dhs-based Surveys (N = )mentioning

confidence: 99%

“…• Overall empowerment (Biswas and Kabir, 2004;Santillán et al, 2004;Williams, 2005;Varghese, 2011;Bhattacharya and Banerjee, 2013;Lombardini et al, 2017;The Hunger Project, 2017;Rafiey et al, 2018;Sharaunga et al, 2019;Sharma and Das, 2021) • Agency (Yount et al, 2016) • Sexual and Reproductive Health (Upadhyay et al, 2014;Hinson et al, 2019;MEASURE Evaluation, 2020;Moreau et al, 2020) • Nutrition (Narayanan et al, 2019;Saha and Narayanan, 2022) • Agriculture (Alkire et al, 2013;Malapit et al, 2017Malapit et al, , 2019 and livestock (Galiè et al, 2019) • Water, sanitation, and hygiene (Dickin et al, 2021) or urban sanitation (Sinharoy et al, 2022) • Psychological (Moubarak et al, 2021)…”

Section: Characteristics Dhs-based Surveys (N = )mentioning

confidence: 99%

“…-Kenya (Kenya National Bureau of Statistics, 2020) -Nigeria (Obayelu and Chime, 2020;Rettig et al, 2020) -Pakistan (Abbas et al, 2021) -Tanzania (Mganga et al, 2021) • South Asia and Sub-Saharan Africa (Sinharoy et al, 2022) • African countries -Kenya, Zambia, and Nigeria (MEASURE Evaluation, 2020) -Ethiopia, Uganda, and Nigeria (Moreau et al, 2020) -Benin, Burkina Faso, Ethiopia, Ghana, Malawi, Mozambique, and Senegal (The Hunger Project, 2017) • Multiple countries (two or more) from different regions -Bangladesh, Guatemala, and Uganda (Alkire et al, 2013) -Bangladesh, Burkina Faso, Ethiopia, Ghana, India, Kenya, Mali, Nepal, Tanzania (Malapit et al, 2019) -Bangladesh and Uganda (Malapit et al, 2017) • Single countries -Bangladesh (Biswas and Kabir, 2004;Williams, 2005) -Burkina Faso (Dickin et al, 2021) -Egypt (Yount et al, 2016) -India (Bhattacharya and Banerjee, 2013;Narayanan et al, 2019;Sharma and Das, 2021;Saha and Narayanan, 2022) -Iran (Rafiey et al, 2018) -Nepal (Hinson et al, 2019) -Oman (Varghese, 2011) -Saudi Arabia (Moubarak et al, 2021) -South Africa (Sharaunga et al, 2019) -Tanzania (Galiè et al, 2019) -Vietnam (Santillán et al, 2004) -United States (Upadhyay et al, 2014) • Methodological only (for any setting of interest) (Lombardini et al, 2017)…”

Section: Geographymentioning

confidence: 99%

“…Analytical approach • Exploratory/confirmatory factor analysis (Phan, 2016;Asaolu et al, 2018;Miedema et al, 2018;Kenya National Bureau of Statistics, 2020;Mganga et al, 2021) • Principal components analysis (Ewerling et al, 2017(Ewerling et al, , 2020Soharwardi and Ahmad, 2020) • Mean of all categories scores (Rettig et al, 2020) • Alkire-Foster method (Obayelu and Chime, 2020) • Binary composite indicator (Abbas et al, 2021) • Multiple category indicator (Annan et al, 2021) • Exploratory/confirmatory factor analysis (Williams, 2005;Upadhyay et al, 2014;Yount et al, 2016;Rafiey et al, 2018;MEASURE Evaluation, 2020;Moubarak et al, 2021;Sharma and Das, 2021) • Exploratory/confirmatory factor analysis and item response theory (Sinharoy et al, 2022) • Principal component analysis and exploratory factor analysis (Moreau et al, 2020) • rincipal component analysis (Sharaunga et al, 2019) • Alkire-Foster method (Alkire et al, 2013;Malapit et al, 2017Malapit et al, , 2019Narayanan et al, 2019;Dickin et al, 2021;Saha and Narayanan, 2022) • Multiple-indicator-multiple-cause model (Bhattacharya and Banerjee, 2013;Yount et al, 2016) • Structural equation model (Bhattacharya and Banerjee, 2013) • Construction method used for the Human Development Index and average of the three dimensions (Varghese, 2011) • Scaling system for quantification of the responses of each sub-indicator (question/variable) and percentage for each indicator (domains); weighted or unweighted mean for the composite index (Biswas and Kabir, 2004) • Sum of the domain-specific categorical variables resulting in a single continuous variable/score …”

Section: Table (Continued)mentioning

confidence: 99%

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Measures of women's empowerment based on individual-level data: a literature review with a focus on the methodological approaches

Costa,

Saad,

Hellwig

et al. 2023

Front. Sociol.

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BackgroundQuantifying women's empowerment has become the focus of attention of many international organizations and scholars. We aimed to describe quantitative indicators of women's empowerment that are based on individual-level data.MethodsIn this scoping review, we searched PubMed, Scopus, Web of Science, Science Direct, Google, and Google Scholar for publications describing the operationalization of measures of women's empowerment.ResultsWe identified 36 studies published since 2004, half of them since 2019, and most from low- and middle-income countries. Twelve studies were based on data from the Demographic and Health Surveys and used 56 different variables from the questionnaires (ranging from one to 25 per study) to measure the overall empowerment of women 15–49 years. One study focused on rural women, two included married and unmarried women, and one analyzed the couple's responses. Factor analysis and principal component analysis were the most common approaches used. Among the 24 studies based on other surveys, ten analyzed overall empowerment, while the others addressed sexual and reproductive health (4 studies), agriculture (3) and livestock (1), water and sanitation (2), nutrition (2), agency (1), and psychological empowerment (1). These measures were mainly based on data from single countries and factor analysis was the most frequently analytical method used. We observed a diversity of indicator definitions and domains and a lack of consensus in terms of what the proposed indicators measure.ConclusionThe proposed women's empowerment indicators represent an advance in the field of gender and development monitoring. However, the empowerment definitions used vary widely in concept and in the domains/dimensions considered, which, in turn influence or are influenced by the adopted methodologies. It remains a challenge to find a balance between the need for a measure suitable for comparisons across populations and over time and the incorporation of country-specific elements.

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Digitalize Work in Pandemic Time: Practices for Remote Working and Job Redesign in Public Organization

Reina

Ventura²,

Cristofaro

et al. 2021

HR Analytics and Digital HR Practices

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Integrated model for women empowerment in rural India

Cited by 36 publications

References 40 publications

Scale Development for COVID-19 Vaccine Hesitancy by Integration of Socio-Demographic and Psychological Factors

Scale Development for COVID-19 Vaccine Hesitancy by Integration of Socio-Demographic and Psychological Factors

Measures of women's empowerment based on individual-level data: a literature review with a focus on the methodological approaches

Digitalize Work in Pandemic Time: Practices for Remote Working and Job Redesign in Public Organization

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