What’s happening in the kitchen? The influence of nutritional knowledge, attitudes, practices (KAP), and kitchen characteristics on women's dietary quality in Ethiopia

What’s happening in the kitchen? The influence of nutritional knowledge, attitudes, practices (KAP), and kitchen characteristics on women's dietary quality in Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article What’s happening in the kitchen? The influence of nutritional knowledge, attitudes, practices (KAP), and kitchen characteristics on women's dietary quality in Ethiopia Temesgen Awoke Yalew, Masresha Tessema, Edward Lahiff This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4269813/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jan, 2025 Read the published version in BMC Nutrition → Version 1 posted 4 You are reading this latest preprint version Abstract Background Low diet quality significantly contributes to public health risks in low-income countries. This situation is particularly concerning for vulnerable groups, such as women and children, who are at increased risk of malnutrition due to inadequate access to proper nutrition. Objective This study aimed to assess the influence of nutrition-related knowledge, attitudes, practices, and kitchen characteristics on women's dietary quality in Ethiopia. Method A population-based cross-sectional survey was conducted from August to September 2022 in five regions and two city administrations in Ethiopia. A multistage stratified cluster sampling method was employed. From ninety-nine enumeration areas, twenty eligible households were selected. A total of 1,980 women aged 15–49 years were included in this survey. The data were collected using a structured questionnaire and analysed using SPSS version 16 computer software. The determinants of diet quality were identified using Poisson, logistic, and ordinary least square regression analyses. Variables with a p-value less than 0.05 were considered to indicate statistical significance. Results The results showed that the average dietary diversity score for women was 3.4. Only 21.5% of the participants achieved the minimum dietary diversity for women (MDD-W), and the mean adequacy ratio for nutrients was 61.6%. The participants’ average nutrition-related knowledge, attitudes, and practices scores were 63%, 39%, and 23%, respectively. The regression analysis showed a positive association between knowledge and attitude, on the one hand, and dietary diversity and the mean nutrient adequacy ratio, on the other hand, which were significant at P < 0.01. Cooking time and propensity to prepare new food were also positively associated with dietary diversity and with minimum dietary diversity, again significant at P < 0.01. Conclusion Our study showed that improved nutrition-related knowledge and a positive attitude toward nutrition significantly influence dietary quality. Additionally, cooking time and the propensity to prepare new foods positively influence diet quality. Nutritional KAP Kitchen food consumption dietary diversity nutrient intake Ethiopia Figures Figure 1 1. Background Across Africa, malnutrition and food insecurity remain significant challenges and are particularly pronounced in Central and Eastern Africa ( 1 ). Ethiopia has significant nutritional problems that pose substantial risks to public health. The 2016 Ethiopian Demographic and Health Survey revealed that malnutrition was highly prevalent among children under the age of five, with 38% being stunted, 24% being underweight, and 10% being wasted; 20% of women of reproductive age were thin, while 8% were overweight or obese, all of which is indicative of a complex landscape of nutrition-related challenges ( 2 ). A key contributor to nutritional inadequacy in Ethiopia is low diet quality, especially among vulnerable groups such as women and children. Diet quality refers to a balanced diet that provides enough energy and essential nutrients ( 3 ). A standard measure of diet quality is dietary diversity, the number of food groups consumed over a given period of time. ( 4 ) ( 5 ). only 7% of Ethiopian women eat the minimum recommended number of food groups ( 6 ). The most commonly consumed food groups are grains (61%), roots and tubers (3.9%), fruits (9.9%), and legumes and nuts (8.4%). The least consumed food groups are eggs (0.3%), dairy products (5%), fleshy foods (1.5%), and oils and fats (1.5%) ( 7 ). The quality of diets in Ethiopia are shaped by various factors that influence people’s food choices and intake. Among these, socioeconomic status is a significant determinant, with wealthier households with larger landholdings being better able to afford and access a diverse range of foods ( 8 ). In previous studies, women's empowerment, education level, and occupational status were found to be critical in determining household dietary quality, as women tend to be the primary caregivers and decision-makers regarding household food choices ( 9 ). Dietary quality can be influenced by family size and household food production, which can affect the availability and accessibility of food within the household ( 8 , 10 ). Nutritional knowledge is an essential determinant of diet quality, as individuals who know the nutritional value of various foods are better equipped to make informed dietary choices that support good health ( 10 – 12 ). Over the past three decades, nutritional status in Ethiopia has steadily improved, albeit at a slow pace. Since 2008, the Ethiopian government has undertaken several nutritional initiatives, including the National Nutrition Strategy and National Nutrition Program parts One and Two, the Seqota Declaration, and the Food and Nutrition Policy ( 13 – 17 ). Recently, nutrition-specific and nutrition-sensitive programs have been implemented. The malnutrition reduction approach focuses mainly on immediate causes, called nutrition-specific interventions. These address the determinants of foetal and child nutritional development, including vitamin A and zinc supplementation, exclusive breastfeeding, dietary diversity promotion, and food fortification ( 18 ). Nutrition-sensitive programs address adequate maternal, household, and community caregiving resources, access to health services, and a safe and hygienic environment, while also incorporating specific nutritional goals and actions( 19 ). Different sectors, such as agriculture, education, and industry, have been involved in mainstreaming nutrition into their priority activities. Exploring the dynamics of the food system and its contributions to diet quality and related gaps is vital to understanding the complex issues of food and nutritional security. It is also crucial to consider the diverse socioeconomic backgrounds and contexts in which people consume food to design effective nutritional interventions. Therefore, comprehensive research into various aspects of the food system is necessary to uncover the underlying factors influencing dietary quality. Several studies have discussed the factors influencing diet quality in general but little research has been conducted into the specific topic of KAP and kitchen characteristics and their impact on dietary quality. Therefore, this research investigates how nutrition-related knowledge, attitudes, practices, and kitchen characteristics influence women’s dietary quality at the national level. This understanding can, in turn, help in the design of effective interventions to improve women’s dietary quality and reduce the burden of diet-related diseases. 2. Literature review The relationships between knowledge, attitudes, and practices and nutrition outcomes have been extensively explored in the scholarly literature. Schwartz (1976) ( 20 ) developed the cognitive effective behaviour theory, which suggests that increasing nutritional knowledge can significantly affect individuals' attitudes and improve their overall nutritional status. This theory suggests that knowledge serves as a mediator between attitudes and practices. Understanding the role of knowledge in shaping attitudes and practices towards nutrition is thus crucial for addressing malnutrition. More recently, a study by Kwol et al. (2020) ( 21 ) proposed a unified theory that suggests that knowledge plays a fundamental role in shaping an individual's attitude toward nutrition and positively influences dietary behaviour. The authors’ model suggests that knowledge is the key driver of attitudes, thereby influencing dietary outcomes. Numerous studies have highlighted the importance of nutrition knowledge in promoting dietary quality ( 22 )( 23 ). Possessing knowledge of nutrition may not, however, translate into the desired outcomes. It is crucial to supplement knowledge with a positive attitude towards healthy eating and adopting appropriate dietary practices for sustainable dietary quality ( 24 )( 25 ). This means that individuals must understand the importance of good nutrition and the benefits of healthy eating habits to achieve optimal health outcomes. In other words, how people think and behave in terms of their diet can significantly impact their health outcomes. Extensive studies have shown a strong relationship between nutrition-related knowledge, attitudes, and practices (KAP) and dietary quality. Studies conducted by Demissie et al. (2020), Dawit et al. (2021), Col et al. (2017), and Mugdha et al. (2020), specifically examined at the nutritional KAP of lactating mothers, showing that mothers' maternal nutritional practices and knowledge were significantly associated with the quality of their diets. Thus, lactating mothers who better understood nutrition and healthy dietary practices were more likely to consume a quality diet. The findings of these studies can have important implications for the health of lactating mothers and their infants, highlighting the need for nutritional education and support for lactating mothers to improve their dietary practices ( 26 )( 27 )( 28 )( 29 ). For specific socio-demography categories, a study conducted by Dorcus et al. (2015) in Kenya revealed that children with moderate nutritional knowledge and poor dietary practices tend to have negative attitudes toward food ( 24 ). Similarly, a study by Jolanda et al. (2020) among Albanian schoolchildren revealed that adequate nutritional knowledge significantly impacts their dietary behaviour and practices ( 30 ). In other words, the more they knew about proper nutrition, the better they could make informed choices about what they ate, leading to healthier lifestyle habits. A study conducted by Susmita et al. (2020) among Bangladesh adolescent girls suggested that improved KAP is strongly associated with nutritional outcomes ( 31 ). Several research studies have explored the effects of enhancing knowledge, attitudes, and practices (KAP) alongside improving nutrition outcomes in different contexts. Zeinab et al. (2014) conducted a study in rural households in Iran( 32 ), while Hongyan et al. (2018) focused on kindergarten teachers in China ( 23 ). Fadwa et al. (2023) carried out research on elderly individuals in Jordan ( 33 ), Xiya et al. (2020) focused on international students in Ireland( 34 ), and Hashem et al. (2020) conducted a study on schoolteachers in Kuwait ( 33 ). These studies all clearly demonstrated the importance of KAP for improving nutritional outcomes. Overall, the literature highlights the importance of a good understanding of nutrition, fostering the promotion of healthy eating habits, and providing people with the requisite resources to adopt appropriate dietary practices for optimal health outcomes. Public interventions can effectively enhance nutritional outcomes and promote overall well-being, even in low-income countries, by addressing knowledge gaps, fostering positive attitudes, and encouraging healthy dietary practices. 3. Methodology 3.1. Study setting, design and timing The Federal Democratic Republic of Ethiopia is located in the Horn of Africa, sharing borders with Eritrea and Djibouti to the north, Somaliland to the northeast, Somalia to the east, Kenya to the south, South Sudan to the west, and Sudan to the northwest. The country is divided into ten regional states and two city administrations. Data were collected in five regions and two city administrations: Amhara, Oromia, Somalia, Southern People and Peoples Nationalities, Sidama, Dri Dawa City Administration, and Addis Ababa City Administration. These areas account for 90.4% of the national population ( 34 ). A population-based cross-sectional survey was conducted from August to September. The objective of the study was to determine the influence of nutrition-related knowledge, attitudes, and practices and kitchen characterises on women’s diet quality. 3.2. Sample size determination Sample size was calculated based on the known prevalence of low dietary diversity. A single population proportion formula was used to estimate the sample size needed regionally based on the prevalence of indicators using a 0.05 desired leave standard error, a 95% confidence level, and a design effect of 1.5. The sample size was adjusted for region-specific average household size, region-specific percentage of the target population, a household response rate of 94.5%, and an individual response rate of 80%. n= \(\frac{{Z}_{\alpha /2}^{2} p(1-p)}{{d}^{2}}\) * DEFE * \(\frac{100}{HHRR}\) * \(\frac{100}{IRR}\) * \(\frac{1}{Ave.HH size}\) * \(\frac{1}{\% of Target PP}\) where, n = sample size, Z 𝞪/2 = standard errors from the mean corresponding to the 95% confidence level = 1.96, P = prevalence, d = allowable error = 0.05, DEFE = design effect = 1.5, Ave. HH size = average household size from each region, %of Target PP = proportion of the target population from each region, HHRR = household response rate (%) = 94.5%, IRR = individual response rate (%) = 80%. After using the formula, we calculated the total sample size to be 1,980. 3.3. Study population The study population was women of reproductive age (15–49 years). The subjects were drawn from eligible households in the study areas. 3.4. Sampling procedures A multistage stratified cluster sampling method was used to choose households for the study. In the first stage, districts were selected from each study region based on a sampling frame developed by the Central Statistical Agency (CSA) for the 2021 Household Income Consumption Expenditure Survey (HICES). ninety-nine enumeration areas (EA) were chosen from selected district using the lottery method. A household list was obtained for each of the selected EAs, which were used as a sampling frame for the final stage of household selection. A household had to contain at least one member of the study target group. From the revised listing, twenty eligible households pre-EA were selected randomly. Table 1 Sample size distribution Region EAs Total HHs Amhara 20 400 Oromia 26 520 Somali 9 180 SNNP 19 380 Sidama 6 120 Addis Ababa 16 320 Dire Dawa 3 60 Total 99 1980 3.5. Survey tools and data collection A structured questionnaire with four modules was designed and administered to gather comprehensive information on respondents' characteristics and various topics related to the food systems and dietary outcomes. The household module was used to collect data on household members' age, sex, education, residency and household-level characteristics such as the source of drinking water, toilet and hand washing facilities, cooking fuel, cookstove type, assets, wealth, and income. The nutrition-related knowledge, attitudes, and practices module followed the guidelines of the Food and Agriculture Organization (FAO) ( 35 ). The topics covered were feeding practices for infants, children, and school-age children; nutrition during pregnancy; prevention of macro and micronutrient deficiencies; and water hygiene and sanitation. The food frequency module was adapted from the Food and Agriculture Organization (FAO)( 36 ) dietary assessment tools and was tailored to fit Ethiopia's list of available food options. It comprises a specific list of food and beverages and offers response categories to indicate the usual frequency of consumption over seven days. The women’s time-use module was sourced from questionnaires from the Ethiopia Central Statistics Agency (CSA) ( 37 ) and was used to gather information on how women allocate their time. The entire questionnaire was created in English but was translated into Amharic and Affan Oromo as spoken in the survey area. The questionnaire was programmed in the CSPro software package using pre coded responses, and tablet computers were used to collect the data. Several procedures were followed during the survey's design and implementation to ensure data quality. First, data collection training modules and field guidelines were prepared. Qualified and experienced data collectors were recruited and underwent two weeks of intensive data collection training. After the training, a mock test was conducted among the participants. Finally, a pretest was conducted in the Oromia region (in Bishoftu, near Addis Ababa) and, after the necessary adjustment, the survey tool was finalized. 3.6. Operational definitions Good knowledge when the participant correctly answered more than 50% of the knowledge questions. Positive attitude when the participant agreed with more than 50% of the attitude questions. Good practices when the participant reported applying more than the mean number of recommended practices. Kitchen characteristics refer to the time spent cooking, the type of cook stove used, the energy source, the water source, the available cooking utensils, and the propensity to prepare new food. 3.7. Method of data analysis We used a combination of quantitative methods to gain insights into the factors affecting women's dietary quality. We used SPSS version 16 statistical software to analyse descriptive statistics such as the mean, median, standard deviation, and percentages of the collected data. To further examine and understand the influencing factors, we deployed multinomial regression, which allowed us to determine the extent to which various factors contributed to the quality of women's diets. Following regression analysis, we conducted post estimation tests to verify the accuracy and validate the assumptions. To check for homoscedasticity, we used Cameron and Trivedi's decomposition test and the Breusch-Pagan/ Cook-Weisberg test for heteroscedasticity, while for multicollinearity we employed the Variance Inflation Factor (VIF) test. 3.7.1. Dietary quality assessment The dietary diversity score (DDS) was calculated by summing the food groups consumed during the last 24 hours ( 5 ). The minimum dietary diversity score (MDD-W) was calculated for a woman who has consumed at least five food groups during the last 24 hours ( 4 ). Nutrient intake was estimated using a seven-day food frequency and calculated with a food composition table developed for Ethiopia ( 38 ). The nutrient adequacy ratio (NAR) was calculated as the women's nutrient intake ratio for both macronutrients and micronutrients relative to the recommended allowance in the Ethiopian Food based dietary guidelines and Dietary Intake Reference ( 36 )( 39 )( 40 ). NAR \(=\frac{\text{A}\text{c}\text{t}\text{u}\text{a}\text{l} \text{N}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t} \text{i}\text{n}\text{t}\text{a}\text{k}\text{e} \text{o}\text{f} \text{a} \text{n}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t} \left(\text{p}\text{e}\text{r} \text{d}\text{a}\text{y}\right)}{\text{R}\text{e}\text{c}\text{o}\text{m}\text{m}\text{e}\text{n}\text{d}\text{e}\text{d} \text{d}\text{a}\text{i}\text{l}\text{y} \text{a}\text{l}\text{l}\text{o}\text{w}\text{a}\text{n}\text{c}\text{e} \text{o}\text{f} \text{t}\text{h}\text{e} \text{n}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t} }\) The mean adequacy ratio (MAR) was calculated as an overall measure of nutrient adequacy. MAR \(=\frac{\sum \text{N}\text{A}\text{R} \left(\text{N}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t} \text{A}\text{d}\text{e}\text{q}\text{u}\text{a}\text{c}\text{y} \text{R}\text{a}\text{t}\text{i}\text{o}\right)}{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{N}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t}\text{s} }\times 100\) 3.7.2. KAP scoring For nutrition-related knowledge , each question was answered with a yes or no response; correct responses were awarded one point, while incorrect responses received a zero score. The average score for the sample was subsequently calculated. The nutrition-related attitudes were rated using a three-point Likert scale comprising 1 (agree), 2 (neutral), and 3 (disagree). The average score was then calculated for the number of statements agreed with. Nutrition-related practices were scored based on recommended health and nutritional practices, earning one point for appropriate practices or zero otherwise. The average score was calculated. 3.7.3. Econometrics approach An econometric approach was used to analyse three distinct dietary outcomes for women, namely dietary diversity (DD), minimum dietary diversity (MDD) and the nutrient mean adequacy ratio (NMAR). The analysis aimed to establish the relationships between these dietary outcomes and various factors such as nutrition-related knowledge, attitudes, practices, and kitchen characteristics. To analyse the first dependent variable, the Women's Diet Diversity Score, we had to consider that it can only take nonnegative integral values. Therefore, a count data modelling approach was necessary and the Poisson model was a suitable choice as it accommodates the discrete nature of the dependent variable. In contrast, the minimum dietary diversity score is a binary value, so the logistic model was appropriate. The mean nutrient adequacy ratio is continuous, and for this result, the ordinary least square method (OLS) method was suitable for modelling. We modelled women's dietary outcomes (DD, MDD, and MNAR) as a function of nutrition-related knowledge, attitudes, and practices, kitchen characteristics, and selected sociodemographic characteristics using the following regression model: \({DDs }_{i}\) = \({\beta }_{0}\) + \({\beta }_{1}{KAP}_{i}\) + \({\beta }_{2}{KC}_{i}\) + \({\beta }_{3}{SD}_{i}\) \(+\) \({\epsilon }_{i}\) \({MDDWs }_{i}\) = \({\beta }_{0}\) + \({\beta }_{1}{KAP}_{i}\) + \({\beta }_{2}{KC}_{i}\) + \({\beta }_{3}{SD}_{i}\) \(+\) \({\epsilon }_{i}\) \({MARs }_{i}\) = \({\beta }_{0}\) + \({\beta }_{1}{KAP}_{i}\) + \({\beta }_{2}{KC}_{i}\) + \({\beta }_{3}{SD}_{i}\) \(+\) \({\epsilon }_{i}\) where \({DDs}_{i}\) is diet diversity score, \({MDDs}_{i}\) is minimum diet diversity score, \({MAR}_{i}\) is the mean adequacy ratio of nutrients, \({KAP}_{i}\) is a vector of variables capturing nutrition-related knowledge, attitude, and practice, \({KC}_{i}\) is a vector of variables capturing kitchen characteristics, \({SD}_{i}\) is a vector of variables capturing socio-demographics, and \({\epsilon }_{i}\) is a random error term. 3.7.4. Hypothesized Variables Dependent variables Dietary quality was measured by the dietary diversity score (DDS), minimum dietary diversity for women (MDD-W), and mean adequacy ratio of nutrients (MAR). DDS is the average number of different food groups consumed by women, while MDD-W refers to women who have consumed at least five of the ten possible food groups. NMAR compares the consumption of nutrients to the age and sex-specific recommended nutrient amount. Independent Variables The independent variables are explanatory factors that facilitate or hinder outcome variables, and include nutrition-related knowledge, attitudes, practices, as well as kitchen characteristics. 4. Results 4.1. Sociodemographic factors Table 2 presents the key sociodemographic characteristics of the study participants. These show that most participants were in their twenties or thirties, with 77% being married. Approximately 37% of the individuals had never attended school, while more than 60% had completed primary education or beyond. Nearly half of the participants identified themselves as Orthodox Christians, while one-third identified as Muslims. The average household size was 4.6 people. Sixty-four percent of the participants were employed (including self-employed individuals), with agriculture being the leading employment sector. Wholesale, retail, education, community and social service followed, while manufacturing industries had the lowest level of participation. Table 2 Sociodemographic characteristics Characteristics (n = 1910) Total (%) Age 15–19 5.2 20–29 37.8 40 − 39 37 40–49 19.9 Marital status Never married 13.1 Married or living together 77.5 Divorced or separated 5.6 Widowed 3.8 Education No education 37.3 Read and write 0.5 Primary 30.4 Secondary 19.8 More than secondary 12 Religion Orthodox 48.8 Muslim 31.9 Protestant 18.6 Catholic/Other Christian 0.4 Other 0.2 Average household size 4.6 Employment Engage in any job (%) 68.47 Employed in agriculture (%) 48.02 4.2. Dietary quality outcomes Figure 1 shows the distribution of the dietary diversity indicators for the women in the survey sample. Most respondents consumed two or three food groups, while less than 10% consumed five food groups or more. Nearly all women consume staple crops (grains, roots, and tubers) and legumes or nuts. The consumption of animal-source foods, however, was minimal. The average dietary diversity score of the group was 3.4, and only 21.57% of the respondents met the FAO MDD-W criteria. The median nutrient intake of the participants is provided in Table 3 , based on the seven-day food frequency questionnaire and calculated using the food composition table for Ethiopia. These computations showed that participants consumed an average of 1,361 kilo calories of energy per day, which is 68% of the recommended amount. This consisted of 37.3 g of protein, or 83% of the recommended amount, and 23.1 g of lipids, or 29.7% of the recommended amount. The intake of minerals was also calculated, and it was found that the participants consumed 400 mg of calcium, or 27% of the recommended amount, 43 mg of iron, which is more than the recommended amount, and 8 mg of zinc, which is 89% of the recommended amount. Additionally, 337 mg of vitamin A retinol equivalent was consumed, which is 34.5% of the recommended amount, along with 0.4 mg of thiamine (44.4% RDA), 0.71 mg of riboflavin (64.5% RDA) and 2.57 mg of niacin (18.3% RDA). The overall mean nutrient adequacy ratio for the sample was 55.84%. Table 3 Nutrient intake Nutrients/24 hr (n = 1910) Median SD NAR% Energy (kcal) 1361.8 539.6 68 Protein (g) 37.3 22.4 83 Fat (g) 23.1 15.6 29.7 Calcium (mg) 351.4 195.2 27 Iron (mg) 44.5 16.7 100 Zinc (mg) 8 3.3 89 VitA RAE (mg) 241.6 323.6 34.5 Thiamine (mg) 0.4 0.2 44.4 Riboflavin (mg) 0.71 0.4 64.5 Niacin (mg) 2.57 1.5 18.3 MAR - - 55.84 4.3. Knowledge, Attitudes, and Practice Table 4 summarizes the statistics on nutrition-related knowledge, attitudes, and practices. A score of 50% indicates a proficient level of knowledge of the subject. The average score for nutrition-related knowledge was 63.9%. For infant and young child feeding the analysis revealed that the respondents had good knowledge, with an average score of 67.4%. The highest knowledge score of 75.3% was observed for malnutrition, and the lowest was for micronutrient deficiency at 45.6%. Table 4 Nutrition-related knowledge, attitudes, and practice score summary statistics Knowledge score (n = 1910) Mean (%) Std. Err. [95% Conf. Interval] Infant Young child feeding 67.43 0.58 64.14 69.43 Micronutrient deficiency 45.61 0.86 43.91 47.31 Malnutrition 75.3 0.55 74.21 76.39 Total 63.94 0.50 62.95 64.94 Attitude score Perceived benefits 65.51 0.68 64.16 66.86 Perceived susceptibility 11.95 0.44 11.07 12.83 Perceived barrier 68.25 0.7 66.89 69.64 Perceived severity 13.4 0.77 11.87 14.93 Total 39.78 0.4 38.99 40.57 Practice score Clean fuel use 13.08 0.96 11.19 14.98 Treated water 14.97 1.04 12.92 17.02 Cooking demonstration participation 2.52 0.36 1.8 3.2 Preservation techniques 53.71 1.1 51.55 55.87 Proper hand washing 34.39 1.09 32.24 36.54 Total 23.03 0.51 21.02 25.05 The analysis of nutrition-related attitudes (Table 4 ) revealed that the average score was 39.8%, which indicates that the respondents did not have a positive attitude toward nutrition. Looking at the individual components in this area, 65.5% of the participants believed that following good nutritional practices would be beneficial, while 68.2% expressed concerns about barriers to engaging in such practices. In contrast, only 13.4% of the respondents believed in the severity of poor nutrition, while 11.9% perceived their susceptibility to nutrition-related health problems. The average score regarding household nutrition-related practices (Table 4 ), which captures the adoption or attendance of various practices in the households, was only 23%. Among the individual components, the application of preservation techniques had the highest score, at 53.7%, while the lowest score recorded was for participation in community-based cooking demonstrations, at just 2.5%. The Scores for clean fuel use and water treatment practices were in between these extremes, at 23% and 24%, respectively. 4.4. Factors related to kitchen characteristics The data in Table 5 revealed that women spent one-fourth of the day on home care activities, on average, with two hours spent on cooking, and one-and-a-half hours dedicated to fetching water and collecting firewood. The primary energy source used in the home was solid biomass fuel, which accounted for 80% of the households. For cooking, 40.5% of the respondents used tripod stone open fires, 37% used a variety of traditional stoves and 2.8% used improved stoves. Only 23% of households were connected to the electricity grid, but this was not commonly used for cooking. Eighty-six percent of the respondents had access to basic drinking water service while the remaining fourteen percent are below the basic services. The average propensity score for preparing new foods was five out of a possible score of ten. Table 5 Kitchen characteristics Variables (n = 1910) Total Time used for home care (mean ± SD) 2.2 ± 2.7 Time used for cooking (mean ± SD) 2.16 ± 1.15 Solid biomass fuel user (%) 80 Tripod stone open fire (%) 40.5 Improved stove user (%) 2.8 Limited kitchen utensils (%) 52.46 Basic kitchen utensils (%) 47.54 Electric grid-connected (%) 23 Basic water service user (%) 86 Propensity to prepare new food (mean ± SD) 5.5 ± 3.2 Table 6 displays the results of regression analysis with the Poisson coefficient for the dietary diversity score in column one, the OLS for the mean adequacy ratio in column two, and the logistic coefficient for minimum dietary diversity for women in column three. The regression analysis (Table 6 ) showed that having completed secondary level education or higher had a significant and positive impact on the diversity of a person’s diet, with improvements of 0.13 and 0.16, respectively, for each education level. However, there was no significant association between higher levels of education and nutrient mean adequacy ratio or minimum dietary diversity for women, except for those with primary education, for which the difference was significant at the 5% level. The analysis also shows that married women have better dietary habits than unmarried women, across the three components of dietary diversity score, nutrient mean adequacy ratio and minimum dietary diversity. This positive impact was significant at the 1% level. Being married improved dietary diversity score by 0.19, nutrient mean adequacy ratio by 0.1, and the minimum diet diversity for women by 3.3%. According to the analysis, women with a good knowledge of nutrition tend to have a better diet. This was shown by a positive and significant association with dietary diversity scores, nutrient mean adequacy ratio, and minimum dietary diversity. For every one-point increase in nutrition-related knowledge, the dietary diversity score improved by 0.45, the mean nutrient adequacy ratio improved by 0.18, and the minimum dietary diversity for women increased by 11.53%. This regression analysis showed (Table 6 ) that having a negative attitude toward nutrition was linked to a lower dietary diversity score, and this correlation was significant at the 1% level. Specifically, for each point decrease in nutrition-related attitudes, there was a decrease of 0.42 in the dietary diversity score. A positive attitude towards nutrition was however, positively associated with minimum dietary diversity among women, and this relationship was significant at the 1% level. For every one-point increase in attitude, women experienced an improvement of 1% in minimum dietary diversity. Nutrition-related practices were positively associated with all dietary quality outcomes, but not significantly so. According to our study (Table 6 ), there was a positive association between cooking time and dietary diversity for women. Cooking time was significantly related to dietary diversity and minimum dietary diversity at the 5% level, while the nutrient mean adequacy ratio had a 1% significance level. With every hour increase in cooking time, there was an improvement in the diet diversity score of 0.03, in the nutrient mean adequacy ratio by 0.02, and in the minimum dietary diversity for women of 1.17%. The study showed that the propensity to prepare new foods was positively associated with all the indicators and was significant at the 1% level. Every additional level of risk-taking propensity increased the diet diversity score by 0.02, the nutrient mean adequacy ratio by 0.01, and the minimum diet diversity for women by 1.12%. Being unable to eat the desired food had a significant negative association with all three dietary indicators at the 1% level. A negative answer reduced the dietary diversity score by 0.19, nutrient mean adequacy ratio by 0.13, and minimum diet diversity for women by 0.28%. Table 6 Regression analysis results Independent variables DDS MAR MDDW Poisson OLS Logistic Coef., sig, CI Coef., sig, CI Odds ratio, sig, CI Educational level Can read and write − .061 (-.659 − .537) .104 (-.193 − .401) 1 Primary .071 * (-.013 − .156) .055 ** (.009 − .101) 1.756 ** (1.139–2.709) Secondary .134 ** (.024 − .245) .022 (-.041-.084) 1.691 * (.956–2.992) More than secondary .161 ** (.03 − .292) .008 (-.07 − .086) 1.564 (.805–3.041) Marital status Married .195 *** (.051 − .34) .107 *** (.032 − .182) 3.376 *** (1.369–8.325) Divorced/Separated/ widow .172 * (-.004 − .348) .069 (-.025 − .162) 2.729* (.944–7.888) Knowledge, Attitude, and Practice Nutrition-related Knowledge .457 *** (.268 − .647) .18 *** (.074 − .287) 11.534 *** (4.33–30.66) Nutrition-related Attitude .424 *** (.638 - − .209) .005 (-.116 − .126) .108 *** (.035 − .331) Nutrition-related Practice .161 * (-.024 − .346) .076 (-.029 − .18) 2.125 (.831–5.431) Kitchen characteristics Cooking time use .036 ** (.008 − .065) .028 *** (.011 − .044) 1.178 ** (1.101–1.365) Women Time use − .001 (-.001 − .009) − .005 (-.01 − .001) .994 (.943–1.048) Basic kitchen utensils .033 (-.04 − .106) .001 (-.04 − .04) .961 (.656–1.409) Propensity to prepare new food .02 *** (.009 − .031) .01 *** (.004 − .016) 1.127 *** (1.062–1.197) Don’t eat food I like/aspire − .196 *** (-.277 - − .11) − .139 *** (-.185 - − .093) − .284 *** (-.192 - − .421) Constant .735 *** (.495 − .976) .641 *** (511 − .771) .018 *** (.004 − .07) *** p < .01, ** p < .05, * p < .1, number of observations 1910 5. Discussion The state of dietary diversity and nutrient intake among the population of the study area is a matter of concern. Despite the government’s prioritization of nutrition-specific and sensitive interventions to address malnutrition, the consumption of diverse diets and optimal nutrient intake is still low. This is particularly true for women of reproductive age. To improve the dietary quality of this population, it is essential to identify the factors that influence nutritional outcomes. Understanding these determinants, such as nutrition-related knowledge, attitudes, practices, and kitchen characteristics, is crucial for developing effective interventions that promote health and nutrition. The primary objective of this study was to examine the associations of KAP and kitchen characteristics with dietary diversity and nutrient intake among women of reproductive age. The present study is the first cross-sectional study of this type at the national level in Ethiopia. 5.1. Nutrition-related knowledge In this study, we evaluated the participants’ level of knowledge of nutritional concepts and dietary quality, by calculating the proportion of correct answers provided by the participants in three crucial categories: infant and young child feeding, micronutrient intake, and malnutrition. The overall score for knowledge was computed by aggregating all these scores. It is recommended to start feeding infants within an hour of birth with exclusive breastfeeding for the first six months of life. After six months, complementary foods that are safe and nutritionally adequate should be given while continuing to breastfeed for up to two years of age or more ( 41 ). According to this study, the knowledge score for infant and young child feeding was 67%. These scores are lower than those of other studies conducted in Ethiopia, such as those in the Somali region (Shebel zone) and Benishangul Gumez region (Assosa woreda) which had scores of 96.1% and 93.8%, respectively ( 42 )( 27 ), and studies in central India and Maharashtra, which had scores of 83.7% and 90.1%, respectively ( 28 )( 29 ). Our score is higher than the score obtained from a study conducted in the South Omo zone, which reported a score of 54.3% ( 43 ). Micronutrients are essential vitamins and minerals that our bodies require in trace amounts to function properly. These nutrients play a crucial role in maintaining our overall health and well-being. Even a minor deficiency of these micronutrients can result in severe and potentially life-threatening health conditions. The findings revealed that the knowledge score for micronutrients was 45.6%. This result is consistent with the findings of previous studies in Ethiopia ( 44 )( 26 ) and greater than those of comparable studies conducted in Sri Lanka and Iran ( 45 )( 32 ). Malnutrition arises when the body does not receive an adequate amount of essential nutrients required for proper growth and functioning. This can occur due to either undernutrition, which results from insufficient nutrients, or overnutrition, which results from consuming more nutrients than the body requires. This study found a knowledge score for malnutrition of 75.3%. This score is higher than that reported in a previous study conducted in Kombolcha city, Amhara region of Ethiopia, which was 57.2% ( 26 ). Scores of 19.5% and 52.2% were reported in comparable studies conducted in Sri Lanka and Iran, respectively ( 45 )( 32 ). The overall score for nutrition-related knowledge was 63.9%. This score had a positive association and was significantly associated (P < 0.001) with the dietary diversity score, nutrient mean adequacy ratio, and minimum diet diversity score. These findings are in line with other studies conducted in Ethiopia ( 46 , 47 ). 5.2. Nutrition-related attitudes The concept of attitudes towards nutrition encompasses an individual's beliefs and emotions about the subject. A three-point Likert scale was used to capture information, and the results were divided into four categories for a more comprehensive analysis. These categories are the benefits of nutrition, which highlights the advantages of nutritional principles; the barriers to nutritional practices, which identify the obstacles that may prevent an individual from adopting better nutrition; the susceptibility to nutritional problems, which refers to the likelihood of an individual developing nutritional deficiencies or disorders; and the severity of dietary complications, which assesses the impact of poor nutrition on an individual's physical and mental health. The overall attitude score was computed by aggregating the scores from the four subcategories. The principles of nutrition foucus on nutrient function, human nutritional requirements, and food sources. In this study, participants were asked about their perception of the importance of these principles, and 68% of them were found to perceive the benefits of nutrition principles. These findings are consistent with those of other studies conducted in Iran, (79.6%), and greater than those conducted in Sri Lanka (46%)( 32 )( 45 ). This study analysed women’s perceptions of the barriers to nutritional principles and practices and found that 68% of the participants perceived barriers to nutritional principles and practices. The study also explored participants' perceptions of susceptibility to nutritional problems and the severity of dietary complications. Interestingly, only 11% of participants perceived themselves as susceptible to nutritional problems and 13% perceived the severity of nutritional problems. This low perception of susceptibility and severity underscores the need for increased awareness and education regarding the potential health consequences of poor nutrition. The study participants had an overall nutrition-related attitude score of 39%, which was positively associated with the dietary diversity score and minimum dietary diversity score at a level of p < 0.01. This implies that a more positive attitude toward nutrition is linked to improved dietary quality, and suggests the need for interventions that address attitudes and promote a diverse and balanced diet. 5.3. Nutrition-related practices Nutrition-related practices refer to activities through which the individuals can influence positive nutritional outcomes. These encompass a range of actions, such as properly washing hands, using clean fuel sources, treating water to make it safe for consumption, participating in cooking demonstrations to learn healthy preparation methods and applying food preservation techniques to prolong the shelf life of food items. We calculated the proportion of such practice participation and aggregated scores from each activity to compute the overall nutrition-related practice score. This study revealed that 34% of participants practiced proper hand washing, which is consistent with the findings of a similar survey conducted in Ethiopia by Zemichale and colleagues ( 48 ). Treatment of water before consumption was 14%, which was higher than the reported value of 6.5% in the 2016 EDHS report ( 2 ). We also found that 13.8% of the survey participants used so-called clean fuel for cooking, which is higher than the reported value of 7.7% in the Mini EDHS 2019 report ( 49 ). We observed that the participation of women in cooking demonstrations, however, was very low, at only 2.5%. 5.4. Kitchen Characteristics In this section, we investigated various kitchen-related characteristics, namely, women's time use in household activities, home care, and cooking. We also examined the cookstove types and sources of energy used in households, the availability of kitchen utensils, and the willingness of women to prepare new foods. The study showed that on average, women spent approximately six hours daily for home care and cooking. These findings are consistent with those of other studies conducted in Ethiopia ( 37 ). In this research, 80% of the participants used solid biomass for cooking. Forty percent of the participants used open tripod stone fires for cooking and only 2.8% of the participants used improved cook stoves. These findings are again consistent with those of previous studies conducted in Ethiopia ( 2 ). Solid biomass fuel and open tripod stone fires pose several health risks; studies have shown a negative association between open-fire pollution and women and child nutrition outcomes ( 50 – 55 ). We examined the willingness of individuals to take risks in preparing new foods. The participants were asked to rate their risk-taking behaviour on a scale ranging from 1 to 10, where 1 represented no risk-taking, and 10 represented an extremely high level of risk-taking. The results showed that the participants scored 5.5, indicating that they were moderately open to trying new approaches and interventions related to food, but not excessively so. Our regression analysis revealed a positive correlation between cooking time, on the one hand, and dietary diversity score, nutrient mean adequacy, and minimum dietary diversity score on the other hand. Risk-taking propensity was positively associated with dietary diversity score, nutrient mean adequacy, and minimum dietary diversity score. These associations were all significant at the 1% level. These finding suggest that there is a strong relationship among the two factors and nutritional outcomes, indicating that more time for cooking and introducing a new food preparation can potentially improve nutritional outcomes for women. 6. Conclusion The quality of diet is a significant nutritional concern for Ethiopia. Despite the government's efforts to address this through various interventions, more progress needs to be made in order to meet the required standard. The findings of this study revealed that the average dietary diversity for women across the study area was relatively low, at 3.2, and that only 21.57% of women reached the minimum dietary diversity. The nutrient mean adequacy ratio (NAR) was 55.84%, far from the required level. This study also explored the factors that influenced dietary quality and revealed that it was positively and significantly influenced by improved nutrition-related knowledge, a positive attitude toward nutrition, additional cooking time and a propensity to prepare new foods. 7. Policy implications This study highlights the importance of considering behavioural and household factors as part of efforts to improve the quality of diets in low-income countries such as Ethiopia. It emphasizes the need for nutrition education, greater awareness, the allocation of more time to cooking, and the introduction of new foods to the common diet. These factors can all contribute to improving dietary quality and, in turn, the overall health and well-being of the population. To improve the dietary quality of women, in particular, it is imperative that the government of Ethiopia, development partners, and other stakeholders prioritize nutrition mainstreaming in their activities and provide opportunities to increase awareness and knowledge of what constitutes a healthy diet. This can be achieved by implementing various formal and informal educational programs specifically targeting women, thus equipping them with the necessary skills and knowledge to implement healthier practices that can enhance the overall quality of diets and promote improved health outcomes for families. It is also crucial to promote healthy cooking methods, introduce new food crops, and provide access to safe drinking water. This comprehensive approach is expected to foster a greater understanding and appreciation of nutrition, encourage a positive mindset, and ultimately lead to healthier lifestyles for individuals and communities. More broadly, it is essential to enhance the capabilities of local institutions involved in nutritional intervention. This can be achieved by implementing innovative practices, which include bolstering the existing health and agriculture extension programmes and enhancing the necessary infrastructure for successful intervention practices. 8. Limitations of the study This study was limited to five of Ethiopia's twelve regions, plus the two federal level city administrations, due to ongoing conflicts and war. While this captured a wide diversity, and the vast majority of the Ethiopian population, this may limit the generalization of the findings. The main goal of the study was to identify the factors that determine women's dietary quality, but it is possible that the study did not explore all relevant external and internal factors that contribute to this. Abbreviations CSA – Central Statistics Agency DDS – Dietary Diversity Score EA – Enumeration area FAO – Food and Agriculture Organization HICES – Household Income Consumption and Expenditure Survey KAP – Knowledge, attitudes, and practices MAR – Mean adequacy ratio MDD-W – Minimum Dietary Diversity for Women NAR – Nutrient adequacy ratio Declarations Ethical approval and consent to participate The study received approval from the Ethiopian Public Health Institute Institutional Review Board (EPHI-IRB) under reference number EPHI 6-13/140. The study involved conducting surveys with women of reproductive age to gather information about their socioeconomic status, food consumption, and knowledge, attitudes, and practices towards nutrition. Informed consent was obtained from all participants, and all procedures were conducted in accordance with relevant guidelines. Consent for Publication Not applicable Availability of data and materials The data used in this study are confidential. The data are available upon reasonable request from the corresponding author. Competing interests The authors declare no competing interests. Funding Not applicable Authors contributions Temesgen Awoke Yalew; conceptualization, design, acquisition, data analysis, interpretation and writing. Masresha Tessema (PhD) conceptualization, design, and acqusation. Edward Lahiff (PhD) conceptualization, design, data analysis, interpretation, and review. All authors review the manuscript. Acknowledgements We would like to express our sincere gratitude to Ethiopian Public Health Institute (EPHI), International Development Research Centre (IDRC), and Policy Study institute (PSI) for supporting our study. We are also acknowledged the study participants. 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Declining wood fuel and implications for household cooking and diets in tigania Sub-county Kenya. Sci African [Internet]. 2020;8:e00417. Available from: https://doi.org/10.1016/j.sciaf.2020.e00417 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Jan, 2025 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Revision requested 23 Apr, 2024 Submission checks completed at journal 17 Apr, 2024 Editor assigned by journal 17 Apr, 2024 First submitted to journal 15 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4269813","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292221478,"identity":"af490cc9-0483-4a0f-a508-590516d24928","order_by":0,"name":"Temesgen Awoke Yalew","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACNvbGhgMfKmzs+NkbgNwCCYhwAh4tfDyHGw/OOJOWLNlzAMg1IEKLnER682HelsOMG26AlBkQ4zCGxIYDvA1pzJIz35hu+GFgwWBw/PADhgcV+LQcbDggucOGj186x+xmD9BhBmfSDBgSzuDRwgj0vuEZoC2zc8xu8IC03OBhYEhsw6OFmbHhQGIb0C83z5jd/APX8g+PFjagloMgLTd4zG4jbGnAo4WHseFgAziQ08puyxhI8EgC/XIg4RhuLfLznz/+/AcclYe33XxTUSfHd/zww4c/anBrwQA8IOIACRpGwSgYBaNgFGABALCVVqUJUkf3AAAAAElFTkSuQmCC","orcid":"","institution":"University College Cork","correspondingAuthor":true,"prefix":"","firstName":"Temesgen","middleName":"Awoke","lastName":"Yalew","suffix":""},{"id":292221479,"identity":"9f40b419-dc6e-4485-92de-3bd707859261","order_by":1,"name":"Masresha Tessema","email":"","orcid":"","institution":"Ethiopian Public Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Masresha","middleName":"","lastName":"Tessema","suffix":""},{"id":292221480,"identity":"b31959d1-2d85-4cf7-b19c-8c63c1d991f1","order_by":2,"name":"Edward Lahiff","email":"","orcid":"","institution":"University College Cork","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"","lastName":"Lahiff","suffix":""}],"badges":[],"createdAt":"2024-04-15 12:44:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4269813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4269813/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-025-00991-w","type":"published","date":"2025-01-21T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55329568,"identity":"79d48e54-0d8c-40f1-b2a9-f59b7df029b1","added_by":"auto","created_at":"2024-04-25 18:59:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4966,"visible":true,"origin":"","legend":"\u003cp\u003eDiet Diversity Score\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4269813/v1/e07e5714d8bd7b3b2e92f224.png"},{"id":74859514,"identity":"e4accc51-59cb-47b0-8e0e-3d3c33834187","added_by":"auto","created_at":"2025-01-27 16:14:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1395643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4269813/v1/c1adb8a1-5ce0-4138-bfc1-45bbfe666cd1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"What’s happening in the kitchen? The influence of nutritional knowledge, attitudes, practices (KAP), and kitchen characteristics on women's dietary quality in Ethiopia","fulltext":[{"header":"1. Background","content":"\u003cp\u003eAcross Africa, malnutrition and food insecurity remain significant challenges and are particularly pronounced in Central and Eastern Africa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Ethiopia has significant nutritional problems that pose substantial risks to public health. The 2016 Ethiopian Demographic and Health Survey revealed that malnutrition was highly prevalent among children under the age of five, with 38% being stunted, 24% being underweight, and 10% being wasted; 20% of women of reproductive age were thin, while 8% were overweight or obese, all of which is indicative of a complex landscape of nutrition-related challenges (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key contributor to nutritional inadequacy in Ethiopia is low diet quality, especially among vulnerable groups such as women and children. Diet quality refers to a balanced diet that provides enough energy and essential nutrients (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A standard measure of diet quality is dietary diversity, the number of food groups consumed over a given period of time. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eonly 7% of Ethiopian women eat the minimum recommended number of food groups (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The most commonly consumed food groups are grains (61%), roots and tubers (3.9%), fruits (9.9%), and legumes and nuts (8.4%). The least consumed food groups are eggs (0.3%), dairy products (5%), fleshy foods (1.5%), and oils and fats (1.5%) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe quality of diets in Ethiopia are shaped by various factors that influence people\u0026rsquo;s food choices and intake. Among these, socioeconomic status is a significant determinant, with wealthier households with larger landholdings being better able to afford and access a diverse range of foods (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In previous studies, women's empowerment, education level, and occupational status were found to be critical in determining household dietary quality, as women tend to be the primary caregivers and decision-makers regarding household food choices (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDietary quality can be influenced by family size and household food production, which can affect the availability and accessibility of food within the household (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Nutritional knowledge is an essential determinant of diet quality, as individuals who know the nutritional value of various foods are better equipped to make informed dietary choices that support good health (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past three decades, nutritional status in Ethiopia has steadily improved, albeit at a slow pace. Since 2008, the Ethiopian government has undertaken several nutritional initiatives, including the National Nutrition Strategy and National Nutrition Program parts One and Two, the Seqota Declaration, and the Food and Nutrition Policy (\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Recently, nutrition-specific and nutrition-sensitive programs have been implemented. The malnutrition reduction approach focuses mainly on immediate causes, called nutrition-specific interventions. These address the determinants of foetal and child nutritional development, including vitamin A and zinc supplementation, exclusive breastfeeding, dietary diversity promotion, and food fortification (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Nutrition-sensitive programs address adequate maternal, household, and community caregiving resources, access to health services, and a safe and hygienic environment, while also incorporating specific nutritional goals and actions(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Different sectors, such as agriculture, education, and industry, have been involved in mainstreaming nutrition into their priority activities.\u003c/p\u003e \u003cp\u003eExploring the dynamics of the food system and its contributions to diet quality and related gaps is vital to understanding the complex issues of food and nutritional security. It is also crucial to consider the diverse socioeconomic backgrounds and contexts in which people consume food to design effective nutritional interventions. Therefore, comprehensive research into various aspects of the food system is necessary to uncover the underlying factors influencing dietary quality. Several studies have discussed the factors influencing diet quality in general but little research has been conducted into the specific topic of KAP and kitchen characteristics and their impact on dietary quality. Therefore, this research investigates how nutrition-related knowledge, attitudes, practices, and kitchen characteristics influence women\u0026rsquo;s dietary quality at the national level. This understanding can, in turn, help in the design of effective interventions to improve women\u0026rsquo;s dietary quality and reduce the burden of diet-related diseases.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe relationships between knowledge, attitudes, and practices and nutrition outcomes have been extensively explored in the scholarly literature. Schwartz (1976) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) developed the cognitive effective behaviour theory, which suggests that increasing nutritional knowledge can significantly affect individuals' attitudes and improve their overall nutritional status. This theory suggests that knowledge serves as a mediator between attitudes and practices. Understanding the role of knowledge in shaping attitudes and practices towards nutrition is thus crucial for addressing malnutrition.\u003c/p\u003e \u003cp\u003eMore recently, a study by Kwol et al. (2020) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) proposed a unified theory that suggests that knowledge plays a fundamental role in shaping an individual's attitude toward nutrition and positively influences dietary behaviour. The authors\u0026rsquo; model suggests that knowledge is the key driver of attitudes, thereby influencing dietary outcomes.\u003c/p\u003e \u003cp\u003eNumerous studies have highlighted the importance of nutrition knowledge in promoting dietary quality (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Possessing knowledge of nutrition may not, however, translate into the desired outcomes. It is crucial to supplement knowledge with a positive attitude towards healthy eating and adopting appropriate dietary practices for sustainable dietary quality (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This means that individuals must understand the importance of good nutrition and the benefits of healthy eating habits to achieve optimal health outcomes. In other words, how people think and behave in terms of their diet can significantly impact their health outcomes.\u003c/p\u003e \u003cp\u003eExtensive studies have shown a strong relationship between nutrition-related knowledge, attitudes, and practices (KAP) and dietary quality. Studies conducted by Demissie et al. (2020), Dawit et al. (2021), Col et al. (2017), and Mugdha et al. (2020), specifically examined at the nutritional KAP of lactating mothers, showing that mothers' maternal nutritional practices and knowledge were significantly associated with the quality of their diets. Thus, lactating mothers who better understood nutrition and healthy dietary practices were more likely to consume a quality diet. The findings of these studies can have important implications for the health of lactating mothers and their infants, highlighting the need for nutritional education and support for lactating mothers to improve their dietary practices (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor specific socio-demography categories, a study conducted by Dorcus et al. (2015) in Kenya revealed that children with moderate nutritional knowledge and poor dietary practices tend to have negative attitudes toward food (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Similarly, a study by Jolanda et al. (2020) among Albanian schoolchildren revealed that adequate nutritional knowledge significantly impacts their dietary behaviour and practices (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In other words, the more they knew about proper nutrition, the better they could make informed choices about what they ate, leading to healthier lifestyle habits. A study conducted by Susmita et al. (2020) among Bangladesh adolescent girls suggested that improved KAP is strongly associated with nutritional outcomes (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Several research studies have explored the effects of enhancing knowledge, attitudes, and practices (KAP) alongside improving nutrition outcomes in different contexts. Zeinab et al. (2014) conducted a study in rural households in Iran(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), while Hongyan et al. (2018) focused on kindergarten teachers in China (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Fadwa et al. (2023) carried out research on elderly individuals in Jordan (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), Xiya et al. (2020) focused on international students in Ireland(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and Hashem et al. (2020) conducted a study on schoolteachers in Kuwait (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). These studies all clearly demonstrated the importance of KAP for improving nutritional outcomes.\u003c/p\u003e \u003cp\u003eOverall, the literature highlights the importance of a good understanding of nutrition, fostering the promotion of healthy eating habits, and providing people with the requisite resources to adopt appropriate dietary practices for optimal health outcomes. Public interventions can effectively enhance nutritional outcomes and promote overall well-being, even in low-income countries, by addressing knowledge gaps, fostering positive attitudes, and encouraging healthy dietary practices.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study setting, design and timing\u003c/h2\u003e \u003cp\u003eThe Federal Democratic Republic of Ethiopia is located in the Horn of Africa, sharing borders with Eritrea and Djibouti to the north, Somaliland to the northeast, Somalia to the east, Kenya to the south, South Sudan to the west, and Sudan to the northwest. The country is divided into ten regional states and two city administrations. Data were collected in five regions and two city administrations: Amhara, Oromia, Somalia, Southern People and Peoples Nationalities, Sidama, Dri Dawa City Administration, and Addis Ababa City Administration. These areas account for 90.4% of the national population (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A population-based cross-sectional survey was conducted from August to September. The objective of the study was to determine the influence of nutrition-related knowledge, attitudes, and practices and kitchen characterises on women\u0026rsquo;s diet quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Sample size determination\u003c/h2\u003e \u003cp\u003eSample size was calculated based on the known prevalence of low dietary diversity. A single population proportion formula was used to estimate the sample size needed regionally based on the prevalence of indicators using a 0.05 desired leave standard error, a 95% confidence level, and a design effect of 1.5. The sample size was adjusted for region-specific average household size, region-specific percentage of the target population, a household response rate of 94.5%, and an individual response rate of 80%.\u003c/p\u003e \u003cp\u003en= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{Z}_{\\alpha /2}^{2} p(1-p)}{{d}^{2}}\\)\u003c/span\u003e\u003c/span\u003e * DEFE *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{100}{HHRR}\\)\u003c/span\u003e\u003c/span\u003e * \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{100}{IRR}\\)\u003c/span\u003e\u003c/span\u003e * \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{Ave.HH size}\\)\u003c/span\u003e\u003c/span\u003e * \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{\\% of Target PP}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere, n\u0026thinsp;=\u0026thinsp;sample size, Z \u003csub\u003e\u0026#120746;/2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;standard errors from the mean corresponding to the 95% confidence level\u0026thinsp;=\u0026thinsp;1.96, P\u0026thinsp;=\u0026thinsp;prevalence, d\u0026thinsp;=\u0026thinsp;allowable error\u0026thinsp;=\u0026thinsp;0.05, DEFE\u0026thinsp;=\u0026thinsp;design effect\u0026thinsp;=\u0026thinsp;1.5, Ave. HH size\u0026thinsp;=\u0026thinsp;average household size from each region, %of Target PP\u0026thinsp;=\u0026thinsp;proportion of the target population from each region, HHRR\u0026thinsp;=\u0026thinsp;household response rate (%)\u0026thinsp;=\u0026thinsp;94.5%, IRR\u0026thinsp;=\u0026thinsp;individual response rate (%)\u0026thinsp;=\u0026thinsp;80%. After using the formula, we calculated the total sample size to be 1,980.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Study population\u003c/h2\u003e \u003cp\u003eThe study population was women of reproductive age (15\u0026ndash;49 years). The subjects were drawn from eligible households in the study areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Sampling procedures\u003c/h2\u003e \u003cp\u003eA multistage stratified cluster sampling method was used to choose households for the study. In the first stage, districts were selected from each study region based on a sampling frame developed by the Central Statistical Agency (CSA) for the 2021 Household Income Consumption Expenditure Survey (HICES). ninety-nine enumeration areas (EA) were chosen from selected district using the lottery method. A household list was obtained for each of the selected EAs, which were used as a sampling frame for the final stage of household selection. A household had to contain at least one member of the study target group. From the revised listing, twenty eligible households pre-EA were selected randomly.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample size distribution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal HHs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmhara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSidama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAddis Ababa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDire Dawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Survey tools and data collection\u003c/h2\u003e \u003cp\u003eA structured questionnaire with four modules was designed and administered to gather comprehensive information on respondents' characteristics and various topics related to the food systems and dietary outcomes.\u003c/p\u003e \u003cp\u003eThe household module was used to collect data on household members' age, sex, education, residency and household-level characteristics such as the source of drinking water, toilet and hand washing facilities, cooking fuel, cookstove type, assets, wealth, and income.\u003c/p\u003e \u003cp\u003eThe nutrition-related knowledge, attitudes, and practices module followed the guidelines of the Food and Agriculture Organization (FAO) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The topics covered were feeding practices for infants, children, and school-age children; nutrition during pregnancy; prevention of macro and micronutrient deficiencies; and water hygiene and sanitation.\u003c/p\u003e \u003cp\u003eThe food frequency module was adapted from the Food and Agriculture Organization (FAO)(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) dietary assessment tools and was tailored to fit Ethiopia's list of available food options. It comprises a specific list of food and beverages and offers response categories to indicate the usual frequency of consumption over seven days.\u003c/p\u003e \u003cp\u003eThe women\u0026rsquo;s time-use module was sourced from questionnaires from the Ethiopia Central Statistics Agency (CSA) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and was used to gather information on how women allocate their time.\u003c/p\u003e \u003cp\u003eThe entire questionnaire was created in English but was translated into Amharic and Affan Oromo as spoken in the survey area. The questionnaire was programmed in the CSPro software package using pre coded responses, and tablet computers were used to collect the data.\u003c/p\u003e \u003cp\u003eSeveral procedures were followed during the survey's design and implementation to ensure data quality. First, data collection training modules and field guidelines were prepared. Qualified and experienced data collectors were recruited and underwent two weeks of intensive data collection training. After the training, a mock test was conducted among the participants. Finally, a pretest was conducted in the Oromia region (in Bishoftu, near Addis Ababa) and, after the necessary adjustment, the survey tool was finalized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Operational definitions\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eGood knowledge\u003c/strong\u003e \u003cp\u003ewhen the participant correctly answered more than 50% of the knowledge questions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePositive attitude\u003c/strong\u003e \u003cp\u003ewhen the participant agreed with more than 50% of the attitude questions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGood practices\u003c/strong\u003e \u003cp\u003ewhen the participant reported applying more than the mean number of recommended practices.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKitchen characteristics\u003c/b\u003e refer to the time spent cooking, the type of cook stove used, the energy source, the water source, the available cooking utensils, and the propensity to prepare new food.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Method of data analysis\u003c/h2\u003e \u003cp\u003eWe used a combination of quantitative methods to gain insights into the factors affecting women's dietary quality. We used SPSS version 16 statistical software to analyse descriptive statistics such as the mean, median, standard deviation, and percentages of the collected data. To further examine and understand the influencing factors, we deployed multinomial regression, which allowed us to determine the extent to which various factors contributed to the quality of women's diets. Following regression analysis, we conducted post estimation tests to verify the accuracy and validate the assumptions. To check for homoscedasticity, we used Cameron and Trivedi's decomposition test and the Breusch-Pagan/ Cook-Weisberg test for heteroscedasticity, while for multicollinearity we employed the Variance Inflation Factor (VIF) test.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.7.1. Dietary quality assessment\u003c/h2\u003e \u003cp\u003eThe \u003cb\u003edietary diversity score (DDS)\u003c/b\u003e was calculated by summing the food groups consumed during the last 24 hours (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eminimum dietary diversity score (MDD-W)\u003c/b\u003e was calculated for a woman who has consumed at least five food groups during the last 24 hours (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNutrient intake\u003c/b\u003e was estimated using a seven-day food frequency and calculated with a food composition table developed for Ethiopia (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cb\u003enutrient adequacy ratio (NAR)\u003c/b\u003e was calculated as the women's nutrient intake ratio for both macronutrients and micronutrients relative to the recommended allowance in the Ethiopian Food based dietary guidelines and Dietary Intake Reference (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNAR \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\frac{\\text{A}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l} \\text{N}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t} \\text{i}\\text{n}\\text{t}\\text{a}\\text{k}\\text{e} \\text{o}\\text{f} \\text{a} \\text{n}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t} \\left(\\text{p}\\text{e}\\text{r} \\text{d}\\text{a}\\text{y}\\right)}{\\text{R}\\text{e}\\text{c}\\text{o}\\text{m}\\text{m}\\text{e}\\text{n}\\text{d}\\text{e}\\text{d} \\text{d}\\text{a}\\text{i}\\text{l}\\text{y} \\text{a}\\text{l}\\text{l}\\text{o}\\text{w}\\text{a}\\text{n}\\text{c}\\text{e} \\text{o}\\text{f} \\text{t}\\text{h}\\text{e} \\text{n}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t} }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe \u003cb\u003emean adequacy ratio (MAR)\u003c/b\u003e was calculated as an overall measure of nutrient adequacy.\u003c/p\u003e \u003cp\u003eMAR\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\frac{\\sum \\text{N}\\text{A}\\text{R} \\left(\\text{N}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t} \\text{A}\\text{d}\\text{e}\\text{q}\\text{u}\\text{a}\\text{c}\\text{y} \\text{R}\\text{a}\\text{t}\\text{i}\\text{o}\\right)}{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{N}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t}\\text{s} }\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.7.2. KAP scoring\u003c/h2\u003e \u003cp\u003eFor nutrition-related \u003cb\u003eknowledge\u003c/b\u003e, each question was answered with a yes or no response; correct responses were awarded one point, while incorrect responses received a zero score. The average score for the sample was subsequently calculated.\u003c/p\u003e \u003cp\u003eThe nutrition-related \u003cb\u003eattitudes\u003c/b\u003e were rated using a three-point Likert scale comprising 1 (agree), 2 (neutral), and 3 (disagree). The average score was then calculated for the number of statements agreed with.\u003c/p\u003e \u003cp\u003eNutrition-related \u003cb\u003epractices\u003c/b\u003e were scored based on recommended health and nutritional practices, earning one point for appropriate practices or zero otherwise. The average score was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.7.3. Econometrics approach\u003c/h2\u003e \u003cp\u003eAn econometric approach was used to analyse three distinct dietary outcomes for women, namely dietary diversity (DD), minimum dietary diversity (MDD) and the nutrient mean adequacy ratio (NMAR). The analysis aimed to establish the relationships between these dietary outcomes and various factors such as nutrition-related knowledge, attitudes, practices, and kitchen characteristics.\u003c/p\u003e \u003cp\u003eTo analyse the first dependent variable, the Women's Diet Diversity Score, we had to consider that it can only take nonnegative integral values. Therefore, a count data modelling approach was necessary and the Poisson model was a suitable choice as it accommodates the discrete nature of the dependent variable. In contrast, the minimum dietary diversity score is a binary value, so the logistic model was appropriate. The mean nutrient adequacy ratio is continuous, and for this result, the ordinary least square method (OLS) method was suitable for modelling. We modelled women's dietary outcomes (DD, MDD, and MNAR) as a function of nutrition-related knowledge, attitudes, and practices, kitchen characteristics, and selected sociodemographic characteristics using the following regression model:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({DDs }_{i}\\)\u003c/span\u003e \u003c/span\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}{KAP}_{i}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{2}{KC}_{i}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}{SD}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({MDDWs }_{i}\\)\u003c/span\u003e \u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}{KAP}_{i}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{2}{KC}_{i}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}{SD}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({MARs }_{i}\\)\u003c/span\u003e \u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}{KAP}_{i}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{2}{KC}_{i}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}{SD}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({DDs}_{i}\\)\u003c/span\u003e\u003c/span\u003e is diet diversity score, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({MDDs}_{i}\\)\u003c/span\u003e\u003c/span\u003e is minimum diet diversity score, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({MAR}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the mean adequacy ratio of nutrients, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({KAP}_{i}\\)\u003c/span\u003e\u003c/span\u003eis a vector of variables capturing nutrition-related knowledge, attitude, and practice, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({KC}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of variables capturing kitchen characteristics, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({SD}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of variables capturing socio-demographics, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{i}\\)\u003c/span\u003e\u003c/span\u003e is a random error term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.7.4. Hypothesized Variables\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDependent variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDietary quality was measured by the dietary diversity score (DDS), minimum dietary diversity for women (MDD-W), and mean adequacy ratio of nutrients (MAR). DDS is the average number of different food groups consumed by women, while MDD-W refers to women who have consumed at least five of the ten possible food groups. NMAR compares the consumption of nutrients to the age and sex-specific recommended nutrient amount.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndependent Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe independent variables are explanatory factors that facilitate or hinder outcome variables, and include nutrition-related knowledge, attitudes, practices, as well as kitchen characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Sociodemographic factors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the key sociodemographic characteristics of the study participants. These show that most participants were in their twenties or thirties, with 77% being married. Approximately 37% of the individuals had never attended school, while more than 60% had completed primary education or beyond. Nearly half of the participants identified themselves as Orthodox Christians, while one-third identified as Muslims. The average household size was 4.6 people. Sixty-four percent of the participants were employed (including self-employed individuals), with agriculture being the leading employment sector. Wholesale, retail, education, community and social service followed, while manufacturing industries had the lowest level of participation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics (n\u0026thinsp;=\u0026thinsp;1910)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRead and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrthodox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtestant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatholic/Other Christian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage household size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngage in any job (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed in agriculture (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Dietary quality outcomes\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of the dietary diversity indicators for the women in the survey sample. Most respondents consumed two or three food groups, while less than 10% consumed five food groups or more. Nearly all women consume staple crops (grains, roots, and tubers) and legumes or nuts. The consumption of animal-source foods, however, was minimal. The average dietary diversity score of the group was 3.4, and only 21.57% of the respondents met the FAO MDD-W criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe median nutrient intake of the participants is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, based on the seven-day food frequency questionnaire and calculated using the food composition table for Ethiopia. These computations showed that participants consumed an average of 1,361 kilo calories of energy per day, which is 68% of the recommended amount. This consisted of 37.3 g of protein, or 83% of the recommended amount, and 23.1 g of lipids, or 29.7% of the recommended amount. The intake of minerals was also calculated, and it was found that the participants consumed 400 mg of calcium, or 27% of the recommended amount, 43 mg of iron, which is more than the recommended amount, and 8 mg of zinc, which is 89% of the recommended amount. Additionally, 337 mg of vitamin A retinol equivalent was consumed, which is 34.5% of the recommended amount, along with 0.4 mg of thiamine (44.4% RDA), 0.71 mg of riboflavin (64.5% RDA) and 2.57 mg of niacin (18.3% RDA). The overall mean nutrient adequacy ratio for the sample was 55.84%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutrient intake\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients/24 hr (n\u0026thinsp;=\u0026thinsp;1910)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNAR%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1361.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e539.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitA RAE (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamine (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiboflavin (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiacin (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e55.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Knowledge, Attitudes, and Practice\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the statistics on nutrition-related knowledge, attitudes, and practices. A score of 50% indicates a proficient level of knowledge of the subject. The average score for nutrition-related knowledge was 63.9%. For infant and young child feeding the analysis revealed that the respondents had good knowledge, with an average score of 67.4%. The highest knowledge score of 75.3% was observed for malnutrition, and the lowest was for micronutrient deficiency at 45.6%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutrition-related knowledge, attitudes, and practice score summary statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge score (n\u0026thinsp;=\u0026thinsp;1910)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[95% Conf.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfant Young child feeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicronutrient deficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAttitude score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived benefits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived susceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived barrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePractice score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean fuel use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreated water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking demonstration participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreservation techniques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProper hand washing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of nutrition-related attitudes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed that the average score was 39.8%, which indicates that the respondents did not have a positive attitude toward nutrition. Looking at the individual components in this area, 65.5% of the participants believed that following good nutritional practices would be beneficial, while 68.2% expressed concerns about barriers to engaging in such practices. In contrast, only 13.4% of the respondents believed in the severity of poor nutrition, while 11.9% perceived their susceptibility to nutrition-related health problems.\u003c/p\u003e \u003cp\u003eThe average score regarding household nutrition-related practices (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which captures the adoption or attendance of various practices in the households, was only 23%. Among the individual components, the application of preservation techniques had the highest score, at 53.7%, while the lowest score recorded was for participation in community-based cooking demonstrations, at just 2.5%. The Scores for clean fuel use and water treatment practices were in between these extremes, at 23% and 24%, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Factors related to kitchen characteristics\u003c/h2\u003e \u003cp\u003eThe data in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e revealed that women spent one-fourth of the day on home care activities, on average, with two hours spent on cooking, and one-and-a-half hours dedicated to fetching water and collecting firewood. The primary energy source used in the home was solid biomass fuel, which accounted for 80% of the households. For cooking, 40.5% of the respondents used tripod stone open fires, 37% used a variety of traditional stoves and 2.8% used improved stoves. Only 23% of households were connected to the electricity grid, but this was not commonly used for cooking. Eighty-six percent of the respondents had access to basic drinking water service while the remaining fourteen percent are below the basic services. The average propensity score for preparing new foods was five out of a possible score of ten.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKitchen characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables (n\u0026thinsp;=\u0026thinsp;1910)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime used for home care (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime used for cooking (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid biomass fuel user (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTripod stone open fire (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved stove user (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited kitchen utensils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic kitchen utensils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectric grid-connected (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic water service user (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropensity to prepare new food (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the results of regression analysis with the Poisson coefficient for the dietary diversity score in column one, the OLS for the mean adequacy ratio in column two, and the logistic coefficient for minimum dietary diversity for women in column three.\u003c/p\u003e \u003cp\u003eThe regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that having completed secondary level education or higher had a significant and positive impact on the diversity of a person\u0026rsquo;s diet, with improvements of 0.13 and 0.16, respectively, for each education level. However, there was no significant association between higher levels of education and nutrient mean adequacy ratio or minimum dietary diversity for women, except for those with primary education, for which the difference was significant at the 5% level. The analysis also shows that married women have better dietary habits than unmarried women, across the three components of dietary diversity score, nutrient mean adequacy ratio and minimum dietary diversity. This positive impact was significant at the 1% level. Being married improved dietary diversity score by 0.19, nutrient mean adequacy ratio by 0.1, and the minimum diet diversity for women by 3.3%.\u003c/p\u003e \u003cp\u003eAccording to the analysis, women with a good knowledge of nutrition tend to have a better diet. This was shown by a positive and significant association with dietary diversity scores, nutrient mean adequacy ratio, and minimum dietary diversity. For every one-point increase in nutrition-related knowledge, the dietary diversity score improved by 0.45, the mean nutrient adequacy ratio improved by 0.18, and the minimum dietary diversity for women increased by 11.53%.\u003c/p\u003e \u003cp\u003eThis regression analysis showed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) that having a negative attitude toward nutrition was linked to a lower dietary diversity score, and this correlation was significant at the 1% level. Specifically, for each point decrease in nutrition-related attitudes, there was a decrease of 0.42 in the dietary diversity score.\u003c/p\u003e \u003cp\u003eA positive attitude towards nutrition was however, positively associated with minimum dietary diversity among women, and this relationship was significant at the 1% level. For every one-point increase in attitude, women experienced an improvement of 1% in minimum dietary diversity.\u003c/p\u003e \u003cp\u003eNutrition-related practices were positively associated with all dietary quality outcomes, but not significantly so. According to our study (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), there was a positive association between cooking time and dietary diversity for women. Cooking time was significantly related to dietary diversity and minimum dietary diversity at the 5% level, while the nutrient mean adequacy ratio had a 1% significance level. With every hour increase in cooking time, there was an improvement in the diet diversity score of 0.03, in the nutrient mean adequacy ratio by 0.02, and in the minimum dietary diversity for women of 1.17%.\u003c/p\u003e \u003cp\u003eThe study showed that the propensity to prepare new foods was positively associated with all the indicators and was significant at the 1% level. Every additional level of risk-taking propensity increased the diet diversity score by 0.02, the nutrient mean adequacy ratio by 0.01, and the minimum diet diversity for women by 1.12%.\u003c/p\u003e \u003cp\u003eBeing unable to eat the desired food had a significant negative association with all three dietary indicators at the 1% level. A negative answer reduced the dietary diversity score by 0.19, nutrient mean adequacy ratio by 0.13, and minimum diet diversity for women by 0.28%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMDDW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoisson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLogistic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef., sig, CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoef., sig, CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds ratio, sig, CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCan read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.061 (-.659 \u0026minus;\u0026thinsp;.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.104 (-.193 \u0026minus;\u0026thinsp;.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071 * (-.013 \u0026minus;\u0026thinsp;.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.055 ** (.009 \u0026minus;\u0026thinsp;.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.756 ** (1.139\u0026ndash;2.709)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.134 ** (.024 \u0026minus;\u0026thinsp;.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.022 (-.041-.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.691 * (.956\u0026ndash;2.992)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.161 ** (.03 \u0026minus;\u0026thinsp;.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.008 (-.07 \u0026minus;\u0026thinsp;.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.564 (.805\u0026ndash;3.041)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.195 *** (.051 \u0026minus;\u0026thinsp;.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.107 *** (.032 \u0026minus;\u0026thinsp;.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.376 *** (1.369\u0026ndash;8.325)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/Separated/ widow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.172 * (-.004 \u0026minus;\u0026thinsp;.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.069 (-.025 \u0026minus;\u0026thinsp;.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.729* (.944\u0026ndash;7.888)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnowledge, Attitude, and Practice\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition-related Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.457 *** (.268 \u0026minus;\u0026thinsp;.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.18 *** (.074 \u0026minus;\u0026thinsp;.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.534 *** (4.33\u0026ndash;30.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition-related Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.424 *** (.638 - \u0026minus;\u0026thinsp;.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.005 (-.116 \u0026minus;\u0026thinsp;.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.108 *** (.035 \u0026minus;\u0026thinsp;.331)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition-related Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.161 * (-.024 \u0026minus;\u0026thinsp;.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.076 (-.029 \u0026minus;\u0026thinsp;.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.125 (.831\u0026ndash;5.431)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKitchen characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking time use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.036 ** (.008 \u0026minus;\u0026thinsp;.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.028 *** (.011 \u0026minus;\u0026thinsp;.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.178 ** (1.101\u0026ndash;1.365)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen Time use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001 (-.001 \u0026minus;\u0026thinsp;.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005 (-.01 \u0026minus;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.994 (.943\u0026ndash;1.048)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic kitchen utensils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.033 (-.04 \u0026minus;\u0026thinsp;.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001 (-.04 \u0026minus;\u0026thinsp;.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.961 (.656\u0026ndash;1.409)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropensity to prepare new food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.02 *** (.009 \u0026minus;\u0026thinsp;.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.01 *** (.004 \u0026minus;\u0026thinsp;.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.127 *** (1.062\u0026ndash;1.197)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDon\u0026rsquo;t eat food I like/aspire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.196 *** (-.277 - \u0026minus;\u0026thinsp;.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.139 *** (-.185 - \u0026minus;\u0026thinsp;.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.284 *** (-.192 - \u0026minus;\u0026thinsp;.421)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.735 *** (.495 \u0026minus;\u0026thinsp;.976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.641 *** (511 \u0026minus;\u0026thinsp;.771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.018 *** (.004 \u0026minus;\u0026thinsp;.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;.1, number of observations 1910\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe state of dietary diversity and nutrient intake among the population of the study area is a matter of concern. Despite the government\u0026rsquo;s prioritization of nutrition-specific and sensitive interventions to address malnutrition, the consumption of diverse diets and optimal nutrient intake is still low. This is particularly true for women of reproductive age. To improve the dietary quality of this population, it is essential to identify the factors that influence nutritional outcomes. Understanding these determinants, such as nutrition-related knowledge, attitudes, practices, and kitchen characteristics, is crucial for developing effective interventions that promote health and nutrition. The primary objective of this study was to examine the associations of KAP and kitchen characteristics with dietary diversity and nutrient intake among women of reproductive age. The present study is the first cross-sectional study of this type at the national level in Ethiopia.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Nutrition-related knowledge\u003c/h2\u003e \u003cp\u003e In this study, we evaluated the participants\u0026rsquo; level of knowledge of nutritional concepts and dietary quality, by calculating the proportion of correct answers provided by the participants in three crucial categories: infant and young child feeding, micronutrient intake, and malnutrition. The overall score for knowledge was computed by aggregating all these scores.\u003c/p\u003e \u003cp\u003eIt is recommended to start feeding infants within an hour of birth with exclusive breastfeeding for the first six months of life. After six months, complementary foods that are safe and nutritionally adequate should be given while continuing to breastfeed for up to two years of age or more (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). According to this study, the knowledge score for infant and young child feeding was 67%. These scores are lower than those of other studies conducted in Ethiopia, such as those in the Somali region (Shebel zone) and Benishangul Gumez region (Assosa woreda) which had scores of 96.1% and 93.8%, respectively (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), and studies in central India and Maharashtra, which had scores of 83.7% and 90.1%, respectively (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our score is higher than the score obtained from a study conducted in the South Omo zone, which reported a score of 54.3% (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicronutrients are essential vitamins and minerals that our bodies require in trace amounts to function properly. These nutrients play a crucial role in maintaining our overall health and well-being. Even a minor deficiency of these micronutrients can result in severe and potentially life-threatening health conditions. The findings revealed that the knowledge score for micronutrients was 45.6%. This result is consistent with the findings of previous studies in Ethiopia (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and greater than those of comparable studies conducted in Sri Lanka and Iran (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMalnutrition arises when the body does not receive an adequate amount of essential nutrients required for proper growth and functioning. This can occur due to either undernutrition, which results from insufficient nutrients, or overnutrition, which results from consuming more nutrients than the body requires. This study found a knowledge score for malnutrition of 75.3%. This score is higher than that reported in a previous study conducted in Kombolcha city, Amhara region of Ethiopia, which was 57.2% (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Scores of 19.5% and 52.2% were reported in comparable studies conducted in Sri Lanka and Iran, respectively (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe overall score for nutrition-related knowledge was 63.9%. This score had a positive association and was significantly associated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with the dietary diversity score, nutrient mean adequacy ratio, and minimum diet diversity score. These findings are in line with other studies conducted in Ethiopia (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Nutrition-related attitudes\u003c/h2\u003e \u003cp\u003eThe concept of attitudes towards nutrition encompasses an individual's beliefs and emotions about the subject. A three-point Likert scale was used to capture information, and the results were divided into four categories for a more comprehensive analysis. These categories are the benefits of nutrition, which highlights the advantages of nutritional principles; the barriers to nutritional practices, which identify the obstacles that may prevent an individual from adopting better nutrition; the susceptibility to nutritional problems, which refers to the likelihood of an individual developing nutritional deficiencies or disorders; and the severity of dietary complications, which assesses the impact of poor nutrition on an individual's physical and mental health. The overall attitude score was computed by aggregating the scores from the four subcategories.\u003c/p\u003e \u003cp\u003eThe principles of nutrition foucus on nutrient function, human nutritional requirements, and food sources. In this study, participants were asked about their perception of the importance of these principles, and 68% of them were found to perceive the benefits of nutrition principles. These findings are consistent with those of other studies conducted in Iran, (79.6%), and greater than those conducted in Sri Lanka (46%)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study analysed women\u0026rsquo;s perceptions of the barriers to nutritional principles and practices and found that 68% of the participants perceived barriers to nutritional principles and practices.\u003c/p\u003e \u003cp\u003eThe study also explored participants' perceptions of susceptibility to nutritional problems and the severity of dietary complications. Interestingly, only 11% of participants perceived themselves as susceptible to nutritional problems and 13% perceived the severity of nutritional problems. This low perception of susceptibility and severity underscores the need for increased awareness and education regarding the potential health consequences of poor nutrition.\u003c/p\u003e \u003cp\u003eThe study participants had an overall nutrition-related attitude score of 39%, which was positively associated with the dietary diversity score and minimum dietary diversity score at a level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. This implies that a more positive attitude toward nutrition is linked to improved dietary quality, and suggests the need for interventions that address attitudes and promote a diverse and balanced diet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Nutrition-related practices\u003c/h2\u003e \u003cp\u003eNutrition-related practices refer to activities through which the individuals can influence positive nutritional outcomes. These encompass a range of actions, such as properly washing hands, using clean fuel sources, treating water to make it safe for consumption, participating in cooking demonstrations to learn healthy preparation methods and applying food preservation techniques to prolong the shelf life of food items. We calculated the proportion of such practice participation and aggregated scores from each activity to compute the overall nutrition-related practice score.\u003c/p\u003e \u003cp\u003eThis study revealed that 34% of participants practiced proper hand washing, which is consistent with the findings of a similar survey conducted in Ethiopia by Zemichale and colleagues (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Treatment of water before consumption was 14%, which was higher than the reported value of 6.5% in the 2016 EDHS report (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). We also found that 13.8% of the survey participants used so-called clean fuel for cooking, which is higher than the reported value of 7.7% in the Mini EDHS 2019 report (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). We observed that the participation of women in cooking demonstrations, however, was very low, at only 2.5%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Kitchen Characteristics\u003c/h2\u003e \u003cp\u003eIn this section, we investigated various kitchen-related characteristics, namely, women's time use in household activities, home care, and cooking. We also examined the cookstove types and sources of energy used in households, the availability of kitchen utensils, and the willingness of women to prepare new foods.\u003c/p\u003e \u003cp\u003eThe study showed that on average, women spent approximately six hours daily for home care and cooking. These findings are consistent with those of other studies conducted in Ethiopia (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this research, 80% of the participants used solid biomass for cooking. Forty percent of the participants used open tripod stone fires for cooking and only 2.8% of the participants used improved cook stoves. These findings are again consistent with those of previous studies conducted in Ethiopia (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Solid biomass fuel and open tripod stone fires pose several health risks; studies have shown a negative association between open-fire pollution and women and child nutrition outcomes (\u003cspan additionalcitationids=\"CR51 CR52 CR53 CR54\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe examined the willingness of individuals to take risks in preparing new foods. The participants were asked to rate their risk-taking behaviour on a scale ranging from 1 to 10, where 1 represented no risk-taking, and 10 represented an extremely high level of risk-taking. The results showed that the participants scored 5.5, indicating that they were moderately open to trying new approaches and interventions related to food, but not excessively so.\u003c/p\u003e \u003cp\u003eOur regression analysis revealed a positive correlation between cooking time, on the one hand, and dietary diversity score, nutrient mean adequacy, and minimum dietary diversity score on the other hand. Risk-taking propensity was positively associated with dietary diversity score, nutrient mean adequacy, and minimum dietary diversity score. These associations were all significant at the 1% level. These finding suggest that there is a strong relationship among the two factors and nutritional outcomes, indicating that more time for cooking and introducing a new food preparation can potentially improve nutritional outcomes for women.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe quality of diet is a significant nutritional concern for Ethiopia. Despite the government's efforts to address this through various interventions, more progress needs to be made in order to meet the required standard. The findings of this study revealed that the average dietary diversity for women across the study area was relatively low, at 3.2, and that only 21.57% of women reached the minimum dietary diversity. The nutrient mean adequacy ratio (NAR) was 55.84%, far from the required level. This study also explored the factors that influenced dietary quality and revealed that it was positively and significantly influenced by improved nutrition-related knowledge, a positive attitude toward nutrition, additional cooking time and a propensity to prepare new foods.\u003c/p\u003e"},{"header":"7. Policy implications","content":"\u003cp\u003eThis study highlights the importance of considering behavioural and household factors as part of efforts to improve the quality of diets in low-income countries such as Ethiopia. It emphasizes the need for nutrition education, greater awareness, the allocation of more time to cooking, and the introduction of new foods to the common diet. These factors can all contribute to improving dietary quality and, in turn, the overall health and well-being of the population. To improve the dietary quality of women, in particular, it is imperative that the government of Ethiopia, development partners, and other stakeholders prioritize nutrition mainstreaming in their activities and provide opportunities to increase awareness and knowledge of what constitutes a healthy diet. This can be achieved by implementing various formal and informal educational programs specifically targeting women, thus equipping them with the necessary skills and knowledge to implement healthier practices that can enhance the overall quality of diets and promote improved health outcomes for families. It is also crucial to promote healthy cooking methods, introduce new food crops, and provide access to safe drinking water. This comprehensive approach is expected to foster a greater understanding and appreciation of nutrition, encourage a positive mindset, and ultimately lead to healthier lifestyles for individuals and communities. More broadly, it is essential to enhance the capabilities of local institutions involved in nutritional intervention. This can be achieved by implementing innovative practices, which include bolstering the existing health and agriculture extension programmes and enhancing the necessary infrastructure for successful intervention practices.\u003c/p\u003e"},{"header":"8. Limitations of the study","content":"\u003cp\u003eThis study was limited to five of Ethiopia's twelve regions, plus the two federal level city administrations, due to ongoing conflicts and war. While this captured a wide diversity, and the vast majority of the Ethiopian population, this may limit the generalization of the findings. The main goal of the study was to identify the factors that determine women's dietary quality, but it is possible that the study did not explore all relevant external and internal factors that contribute to this.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCSA \u0026ndash; Central Statistics Agency\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDDS \u0026ndash; Dietary Diversity Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEA \u0026ndash; Enumeration area\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFAO \u0026ndash; Food and Agriculture Organization\u003c/p\u003e\n\u003cp\u003eHICES \u0026ndash; Household Income Consumption and Expenditure Survey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKAP \u0026ndash; Knowledge, attitudes, and practices\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAR \u0026ndash; Mean adequacy ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDD-W \u0026ndash; Minimum Dietary Diversity for Women\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNAR \u0026ndash; Nutrient adequacy ratio\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received approval from the Ethiopian Public Health Institute Institutional Review Board (EPHI-IRB) under reference number EPHI 6-13/140. The study involved conducting surveys with women of reproductive age to gather information about their socioeconomic status, food consumption, and knowledge, attitudes, and practices towards nutrition. Informed consent was obtained from all participants, and all procedures were conducted in accordance with relevant guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are confidential. The data are available upon reasonable request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemesgen Awoke Yalew; conceptualization, design, acquisition, data analysis, interpretation and writing. Masresha Tessema (PhD) conceptualization, design, and acqusation. Edward Lahiff (PhD) conceptualization, design, data analysis, interpretation, and review. All authors review the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Ethiopian Public Health Institute (EPHI), International Development Research Centre (IDRC), and Policy Study institute (PSI) for supporting our study. We are also acknowledged the study participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Food Science and Nutrition Research Directorate, Ethiopian Public Health Institute, Addis Ababa, Ethiopia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Food Business and Development, University College Cork, Cork, Ireland\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFAO F and AO of the UN, ECA, AUC. 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Available from: https://doi.org/10.1016/j.sciaf.2020.e00417\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nutritional KAP, Kitchen, food consumption, dietary diversity, nutrient intake, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-4269813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4269813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLow diet quality significantly contributes to public health risks in low-income countries. This situation is particularly concerning for vulnerable groups, such as women and children, who are at increased risk of malnutrition due to inadequate access to proper nutrition.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the influence of nutrition-related knowledge, attitudes, practices, and kitchen characteristics on women's dietary quality in Ethiopia.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA population-based cross-sectional survey was conducted from August to September 2022 in five regions and two city administrations in Ethiopia. A multistage stratified cluster sampling method was employed. From ninety-nine enumeration areas, twenty eligible households were selected. A total of 1,980 women aged 15\u0026ndash;49 years were included in this survey. The data were collected using a structured questionnaire and analysed using SPSS version 16 computer software. The determinants of diet quality were identified using Poisson, logistic, and ordinary least square regression analyses. Variables with a p-value less than 0.05 were considered to indicate statistical significance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed that the average dietary diversity score for women was 3.4. Only 21.5% of the participants achieved the minimum dietary diversity for women (MDD-W), and the mean adequacy ratio for nutrients was 61.6%. The participants\u0026rsquo; average nutrition-related knowledge, attitudes, and practices scores were 63%, 39%, and 23%, respectively. The regression analysis showed a positive association between knowledge and attitude, on the one hand, and dietary diversity and the mean nutrient adequacy ratio, on the other hand, which were significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Cooking time and propensity to prepare new food were also positively associated with dietary diversity and with minimum dietary diversity, again significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study showed that improved nutrition-related knowledge and a positive attitude toward nutrition significantly influence dietary quality. Additionally, cooking time and the propensity to prepare new foods positively influence diet quality.\u003c/p\u003e","manuscriptTitle":"What’s happening in the kitchen? The influence of nutritional knowledge, attitudes, practices (KAP), and kitchen characteristics on women's dietary quality in Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 18:59:44","doi":"10.21203/rs.3.rs-4269813/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-23T11:52:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-17T08:01:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-17T08:01:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2024-04-15T12:42:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b9a654b-f336-434b-bb4a-6ab1dc471ea8","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T16:12:25+00:00","versionOfRecord":{"articleIdentity":"rs-4269813","link":"https://doi.org/10.1186/s40795-025-00991-w","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2025-01-21 15:57:46","publishedOnDateReadable":"January 21st, 2025"},"versionCreatedAt":"2024-04-25 18:59:44","video":"","vorDoi":"10.1186/s40795-025-00991-w","vorDoiUrl":"https://doi.org/10.1186/s40795-025-00991-w","workflowStages":[]},"version":"v1","identity":"rs-4269813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4269813","identity":"rs-4269813","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}