Determinants Of Membership Research Articles

Evolution and drivers of farmer producer organizations in Karnataka: Trends and membership determinants

Evolution and drivers of farmer producer organizations in Karnataka: Trends and membership determinants

Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns.

Interindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden. Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression. Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership. Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.

Factors Influencing the Adoption of Urban Agriculture and Its Effects on Household Food Security in Southwestern Nigeria

Given Nigeria's growing population and the rapid rural-to-urban migration, it is crucial to examine various initiatives that can consistently support and improve food security in urban areas. This study examined one of these initiatives, which has proven to be an effective strategy in some areas of the world. Factors influencing the adoption of urban agriculture and its effect on the food security of households were examined in this study. A multi-stage sampling technique was used for data collection among 325 respondents. The analysis was conducted using descriptive statistics, probit, and ordered probit regression models. The descriptive statistics revealed that about 59% of respondents are engaged in urban agriculture. Among the determinants of urban agriculture adoption, age, land ownership, land size, and association membership were observed to positively determine the adoption of urban agriculture, while years of education, access to credit, and income negatively influence its adoption. The ordered probit regression model was used to assess the factors that influence the food security of farming households. Covariates including age and household sizes were found to negatively influence food security. Conversely, gender, education, land ownership rights, land size, credit access, association membership, and urban agriculture positively influence household food security. It was therefore concluded that urban agriculture is an effective means to improve household food security in Southwestern Nigeria. However, increasing awareness and education about the benefits of urban agriculture is crucial, as people often abandon this practice when they have increased access to credit and income. Furthermore, policies toward promoting land ownership and equality in access to resources should be implemented, as this will promote easy participation in urban agriculture.

Determinants of smallholder membership in farmers’ groups in the pigeon pea-based farming system in Uganda

BackgroundMembership in farmers’ group (FGs) is an important social aspect of agricultural development. Little is known about the relative importance and benefits from participation in FGs in Uganda, specifically for pigeon pea smallholders in northern Uganda.MethodsWe conducted a cross-sectional survey with 257 smallholders to examine the factors that motivate farmer’s group membership and its influence on pigeon pea yield and technical efficiency (TE) in northern Uganda. We applied the Stochastic Production Frontier (SPF) model and complementary models to assess membership impact on pigeon pea yield and TE determinants, respectively. TE is defined as the degree to which smallholders use the minimum feasible farm inputs to produce a given level of pigeon pea grain.ResultsResults show that FG members were generally older and more experienced, and had better access to extension services (76%) and credit (43%) compared to non-members. Smallholders’ age, access to agricultural training, extension services, and the distance travelled to market centres were statistically significant (p < 0.05), thus limiting the likelihood of FG membership. TE for both groups was low and quite similar, at 63% and 59% for members and non-members, respectively, implying that both groups did not use the available farm resources to maximize pigeon pea yield.ConclusionOur results confirm that FG membership enhances smallholder’s access to extension, agricultural training services and credit. For northern Uganda, the study provides recommendations for increased government investment in the human, financial and physical capacity of extension agents as a strategy for rural development and improved livelihoods. In addition, policy incentives can encourage farmers to seek membership in farmers’ groups and related associations.

Cluster membership analysis with supervised learning and N-body simulations

Context. Membership analysis is an important tool for studying star clusters. There are various approaches to membership determination, including supervised and unsupervised machine-learning (ML) methods. Aims. We perform membership analysis using the supervised ML approach. Methods. We trained and tested our ML models on two sets of star cluster data: snapshots from N-body simulations, and 21 different clusters from the Gaia Data Release 3 data. Results. We explored five different ML models: random forest (RF), decision trees, support vector machines, feed-forward neural networks, and K-nearest neighbors. We find that all models produce similar results, and the accuracy of RF is slightly better. We find that a balance of classes in the datasets is optional for a successful learning. The classification accuracy strongly depends on the astrometric parameters. The addition of photometric parameters does not improve the performance. We find no strong correlation between the classification accuracy and the cluster age, mass, and half-mass radius. At the same time, models trained on clusters with a larger number of members generally produce better results.

Determinants of membership to agricultural cooperatives in eastern Democratic Republic of Congo: An exploratory study of the coffee sector in South Kivu province

Determinants of membership to agricultural cooperatives in eastern Democratic Republic of Congo: An exploratory study of the coffee sector in South Kivu province

Predictors of cognitive aging profiles over 15 years: A longitudinal population-based study.

The present study aimed to characterize profiles of cognitive aging and how these can be predicted from interindividual differences in demographic, lifestyle, health, and genetic factors. The participants were 1,966 older adults (mean baseline age = 71.6 years; 62.9% female), free from dementia at baseline and with at least two cognitive assessments over the 15-year follow-up, from the population-based Swedish National Study on Aging and Care in Kungsholmen. The cognitive assessment comprised tests of semantic and episodic memory, letter and category fluency, perceptual speed, and executive function. First, we estimated the level and change within each of the cognitive domains with linear mixed effect models, based on which we grouped our sample into participants with "maintained high cognition," "moderate cognitive decline," or "accelerated cognitive decline." Second, we analyzed determinants of group membership within each cognitive domain with multinomial logistic regression. Third, group memberships within each cognitive domain were used to derive general cognitive aging profiles with latent class analysis. Fourth, the determinants of these profile memberships were analyzed with multinomial logistic regression. Follow-up analyses targeted profiles and predictors specifically related to the rate of cognitive change. We identified three latent profiles of overall cognitive performance during the follow-up period with 31.6% of the sample having maintained high cognition, 50.6% having moderate cognitive decline, and 17.8% having accelerated cognitive decline. In multiadjusted analyses, maintained high cognition was predicted by female sex, higher education, and faster walking speed. Smoking, loneliness, and being an ε4 carrier were associated with a lower likelihood of maintained high cognition. Higher age, diagnosis of diabetes, depression, and carrying the apolipoprotein E ε4 allele increased the likelihood of accelerated cognitive decline. Factors at baseline that could significantly predict profile membership within the specific cognitive domains included age, sex, years of education, walking speed, diabetes, and the ε4 allele. Of note, these factors differed across cognitive domains. In sum, we identified demographic, lifestyle, health, and genetic factors of interindividual differences in domain-specific and general cognitive aging profiles, some of which are modifiable. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Novel Preparation Approach for Supervised ML Membership Determination of Open Clusters

Machine Learning methods have emerged as powerful tools for analyzing stellar clusters, which pose significant challenges. techniques such as DBSCAN and GMM have advanced remarkably in this domain. However, these clustering techniques exhibit imperfections and limitations, highlighting the need for careful data tuning and consideration of data characteristics to ensure meaningful result. The utilization of supervised Machine Learning techniques for membership determination of the stellar clusters, especially open clusters, can lead to more accurate results. However, the absence of dataset for training on an open cluster presents a significant hurdle. To address the problem, we’ve introduced a novel approach to generate a labeled dataset for training the super- vised Machine Learning models. Our approach leverages data from Gaia DR3 Catalog, which provides precise astrometric and photometric measurements for millions stars in Milky Way, to construct a comprehensive dataset. Our findings have significant implications for future astronomical research. By using Supervised machine learning techniques, we can achieve more accurate and efficient membership determination for stellar clusters, which can lead to a better understanding of the formation and evolution of galaxies. Our method not only enhances the accuracy of membership determination but also provides insights into the underlying data characteristics that influence cluster analysis.

Healthcare use of young Finnish adults with mental disorders – profiles and profile membership determinants

BackgroundComprehensive, timely, and integrated primary care services have been proposed as a response to the increased demand for mental health and substance use services especially among young people. However, little is known about the care utilization patterns of young people with mental and substance use disorders. Our aim was to characterize profiles of care use in young Finnish adults with mental or substance use disorders, and the potential factors associated with the service use profiles.MethodsPrimary and specialized care visits of young adults (16–29 years) diagnosed with a psychiatric or a substance use disorder (n = 7714) were retrieved from the national health care register from years 2020 and 2021. K-Means clustering was used to detect different profiles based on the utilization of care services. Multinomial logistic regression was used to analyze the factors associated with different profiles of care use.ResultsFive different profiles were identified: low care use (75%), and use of principally primary health care (11%), student health services (9%), psychiatric services (5%), or substance use services (1%). Female gender was associated with membership in the primary health care focused profiles (OR 2.58 and OR 1.99), and patients in the primary health care and student health services profiles were associated with a better continuity of care (OR 1.04 and OR 1.05). Substance use disorders were associated with psychiatric service use (OR: 2.51) and substance use services (OR: 58.91). Living in smaller municipalities was associated with lower service use when comparing to the largest city.ConclusionsYoung adults diagnosed with a psychiatric or a substance use disorder had remarkably different and heterogeneous care patterns. Most of the participants had low care utilization, indicating potential gaps in service use and care needs. Measures should be taken to ensure equal access to and availability of mental health services. The profiles that utilized the most services highlights the importance of integrated services and patient-oriented improvement of treatment.

The Origin of Young Stellar Populations in NGC 1783: Accretion of External Stars

The presence of young stellar populations in the Large Magellanic Cloud cluster NGC 1783 has caught significant attention, with suggestions ranging from it being a genuine secondary stellar generation to a population of blue straggler stars or simply contamination from background stars. Thanks to multiepoch observations with the Hubble Space Telescope, proper motions for stars within the field of NGC 1783 have been derived, thus allowing accurate cluster membership determination. Here, we report that the younger stars within NGC 1783 indeed belong to the cluster and their spatial distribution is more extended compared to the bulk of the older stellar population, consistent with previous studies. Through N-body simulations, we demonstrate that the observed characteristics of the younger stars cannot be explained solely by blue straggler stars in the context of the isolated dynamical evolution of NGC 1783. Instead, accretion of the external, low-mass stellar system can better account for both the inverse spatial concentration and the radial velocity isotropy of the younger stars. We propose that NGC 1783 may have accreted external stars from low-mass stellar systems, resulting in a mixture of external younger stars and blue straggler stars from the older bulk population, thereby accounting for the characteristics of the younger sequence.

Molecular Endotypes of Idiopathic Pulmonary Fibrosis: A Latent Class Analysis of Two Multicenter Observational Cohorts.

Rationale: Idiopathic pulmonary fibrosis (IPF) causes irreversible fibrosis of the lung parenchyma. Although antifibrotic therapy can slow IPF progression, treatment response is variable. There exists a critical need to develop a precision medicine approach to IPF. Objectives: To identify and validate biologically driven molecular endotypes of IPF. Methods: Latent class analysis (LCA) was independently performed in prospectively recruited discovery (n = 875) and validation (n = 347) cohorts. Twenty-five plasma biomarkers associated with fibrogenesis served as class-defining variables. The association between molecular endotype and 4-year transplant-free survival was tested using multivariable Cox regression adjusted for baseline confounders. Endotype-dependent differential treatment response to future antifibrotic exposure was then assessed in a pooled cohort of patients naive to antifibrotic therapy at the time of biomarker measurement (n = 555). Measurements and Main Results: LCA independently identified two latent classes in both cohorts (P < 0.0001). WFDC2 (WAP four-disulfide core domain protein 2) was the most important determinant of class membership across cohorts. Membership in class 2 was characterized by higher biomarker concentrations and a higher risk of death or transplant (discovery, hazard ratio [HR], 2.02; 95% confidence interval [CI], 1.64-2.48; P < 0.001; validation, HR, 1.95; 95% CI, 1.34-2.82; P < 0.001). In pooled analysis, significant heterogeneity in treatment effect was observed between endotypes (P = 0.030 for interaction), with a favorable antifibrotic response in class 2 (HR, 0.64; 95% CI, 0.45-0.93; P = 0.018) but not in class 1 (HR, 1.19; 95% CI, 0.77-1.84; P = 0.422). Conclusions: In this multicohort study, we identified two novel molecular endotypes of IPF with divergent clinical outcomes and responses to antifibrotic therapy. Pending further validation, these endotypes could enable a precision medicine approach for future IPF clinical trials.

Membership Study of Open Cluster NGC 6134 using HDBSCAN Clustering Algorithm

Abstract We present the results of our study on the membership of open star clusters in the southern hemisphere, NGC 6134. The position of NGC 6134 is located near the galaxy’s plane, so the apparent position in the sky is in an area with high stellar density. This poses a challenge in distinguishing between cluster member stars and field stars. For this purpose, we use the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm on astrometric data from Gaia Data Release 3 (DR3). We take stars as samples up to a distance of 90 parsecs from the cluster’s center as membership determination samples. The spatial scope of this sample is intentionally larger than previous studies on this star cluster, as some recent membership studies have concluded that the size of star clusters turns out to be larger than what has been reported. The results of our study produced a consistent number of members with the results of other studies with the same sample size. The clustering algorithm was then performed multiple times using different values of the radius sample. We determined that NGC 6134 has 888 members (membership probability ≥ 0.5), with a radius of 30 parsecs from the cluster’s center.

Unionization of retired workers in Europe

We shed light on an understudied group: retirees in unions. Using representative individual-level data of 19 European countries, we find that the share of retirees in unions and the union density of retirees increased between 2008 and 2020. Econometric analyses indicate that on average retired workers’ probability of union membership is 17 percentage points lower than that of active workers, with some variation among countries. This finding is consistent with social custom models and cost–benefit considerations, but it partly questions the inter-generational solidarity model put forward in the literature. We further find that some determinants of union membership differ between active and retired workers; for instance, the (former) status of being a full-time or blue-collar worker is only statistically significant for active but not for retired workers. Overall, standard membership models better explain the unionization of active than retired workers.

Trajectories of cognitive function among people aged 45 years and older living with diabetes in China: Results from a nationally representative longitudinal study (2011~2018).

Diabetes is associated with decline of cognitive function. Exploring different trajectories of cognitive function occurring in people with diabetes is important to improved prognosis. This study aimed to investigate differential patterns of trajectories of cognitive function and baseline determinants of trajectory group membership utilizing data from middle-aged and older Chinese adults with diabetes. Participants of the Chinese Health And Retirement Longitudinal Study (CHARLS) aged 45 years and above received biennial assessments between 2011 and 2018. The primary outcome was overall cognitive function score operationalized as sum of mental intactness and episodic memory scores derived from the Telephone Interview of Cognitive Status (TICS). A weighted growth mixture model was used to estimate cognitive function trajectories of CHARLS participants with diabetes, and baseline factors associated with trajectory group membership were investigated with weighted multinomial logistic regression. Data from 1,463 participants with diabetes aged 45 years and above were analyzed, a three-group trajectory model showed the best fit for overall cognitive scores: low baseline, linear declining (22.1%); moderate baseline, linear declining (37.5%) and high-stable (40.3%). Older participants, females, participants with low education, with nighttime sleep <6 h, without daytime napping habits, and with depressive symptoms were at a higher risk of unfavorable cognitive function trajectories. We identified heterogeneous trajectories of cognitive function among middle-aged and older people living with diabetes in China. Socially vulnerable groups including females, rural residents, and those with low education were at a higher risk for unfavorable trajectories. In health programs aimed at preventing and mitigating cognitive decline in individuals with diabetes more attention should be given to vulnerable groups. Reduced nighttime sleep, lack of daytime napping, and depressive symptoms appear to be modifiable risk factors.

Priorities for health outcomes in glaucoma in an ethnically diverse UK cohort: an observational study

ObjectivesTo assess whether patients from minority ethnic groups have different perceptions about the quality-of-life outcomes that matter most to them.DesignCross-sectional observational study.SettingHigh volume eye centres serving the most ethnically diverse...

BSEC Method for Unveiling Open Clusters and its Application to Gaia DR3: 83 New Clusters

Open clusters (OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques, including some machine-learning algorithms, available for the exploration of OCs. However, each method has its limitations and therefore, different approaches to discovering OCs hold significant values. We develop a comprehensive approach method to automatically explore the data space and identify potential OC candidates with relatively reliable membership determination. This approach combines the techniques of Hierarchical Density-Based Spatial Clustering of Applications with Noise, Gaussian mixture model, and a novel cluster member identification technique, color excess constraint. The new method exhibits efficiency in detecting OCs while ensuring precise determination of cluster memberships. Because the main feature of this technique is to add an extra constraint (EC) for the members of cluster candidates using the homogeneity of color excess, compared to typical blind search codes, it is called Blind Search-Extra Constraint (BSEC) method. It is successfully applied to the Gaia Data Release 3, and 83 new OCs are found, whose color–magnitude diagrams (CMDs) are fitted well to the isochrones. In addition, this study reports 621 new OC candidates with discernible main sequence or red giant branch. It is shown that BSEC technique can discard some false negatives of previous works, which takes about three percentage of known clusters. It shows that as an EC, the color excess (or two-color) constraint is useful for removing fake cluster member stars from the clusters that are identified from the positions and proper motions of stars, and getting more precise CMDs, when differential reddening of member stars of a cluster is not large (e.g., ΔE(G BP − G RP) < 0.5 mag). It makes the CMDs of 15% clusters clearer (in particular for the region near turnoff) and therefore is helpful for CMD and stellar population studies. Our result suggests that the color excess constraint is more appropriate for clusters with small differential reddening, such as globular clusters or older OCs, and clusters that the distances of member stars cannot be determined accurately.

Membership determination in open clusters using the DBSCAN Clustering Algorithm

In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC 2264, NGC 2682, NGC 2244, NGC 3293, NGC 6913, NGC 7142, IC 1805, NGC 6231, NGC 2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as G∼20 mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters.

Abstract 7440: Symptom network analysis and unsupervised clustering of a cohort of oncology patients identifies drivers of symptom burden and patient subgroups with distinct symptom patterns

Abstract Purpose: Inter-individual variability in oncology patients’ symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate their symptom patterns and drivers of symptom burden. Methods: Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and subgroups identified through unsupervised clustering of symptom co-severity patterns. Symptom network characteristics were analyzed and compared using permutation-based statistical tests. Demographic and clinical characteristic differences between subgroups were assessed using multinomial logistic regression. Results: Network analysis of the entire cohort revealed three core symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network, suggesting that it plays a crucial role in driving overall symptom burden and may be the most salient to target clinically. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The first subgroup represented a baseline cohort of patients who are most affected by common constitutional symptoms of cancer care. The second subgroup represented patients with severe overall symptom burden. The third subgroup represented patients primarily experiencing neuropathy. The fourth subgroup represented patients with advanced disease and treatment-related symptoms but relatively high quality of life. The fifth subgroup represented younger patients with pronounced psychological disease burden. The sixth subgroup represented patients with minimal symptom burden. Age, treatment status, and Karnofsky Performance Score were the strongest determinants of subgroup membership, but their interplay (in one case even with ethnicity) reaffirms the complex multi-factorial nature of the symptom experience. For each subgroup, symptoms with the highest network centralities that should be prioritized for management were identified. Conclusions: This study provides a translational research framework for clinicians to better understand and address the symptom care needs of oncology patients. The framework combines unbiased stratification of patients and in-depth modeling of symptom relationships to capture the heterogeneity in their experiences. When validated, the identified core symptoms and associated clinical determinants may inform precision-based symptom management. Citation Format: Brandon H. Bergsneider, Terri S. Armstrong, Yvette P. Conley, Bruce Cooper, Marilyn Hammer, Jon D. Levine, Steven Paul, Christine Miaskowski, Orieta Celiku. Symptom network analysis and unsupervised clustering of a cohort of oncology patients identifies drivers of symptom burden and patient subgroups with distinct symptom patterns [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7440.

Joint Trajectories of Depression and Rumination: Experiential Predictors and Risk of Nonsuicidal Self-Injury

Nonsuicidal self-injury (NSSI) is common in adolescence. Rumination is a key risk factor and often co-occurs with depressive symptoms. This is the first study to examine the joint longitudinal trajectories of rumination and depressive symptoms as predictors of NSSI, and the adverse experiences associated with these trajectories. A community sample of 1,835 adolescents (55.9% male participants, 12.3 ± 0.5 years of age) completed questionnaires to assess adverse childhood experiences, rumination, depressive symptoms, and NSSI. Assessments were made 4 times over 18 months. A parallel process growth mixture model showed that youth with high trajectories of rumination but low trajectories of depression had moderately increased odds of NSSI (2.43-fold, 95% CI 1.53-3.91) compared with adolescents with low trajectories of both rumination and depression. Odds ratios (ORs) in adolescents with low trajectories of rumination but increasing or high trajectories of depression were similarly elevated, suggesting that high trajectories of rumination or depression were risk factors in isolation. However, odds were 10.06-fold greater (95% CI 5.68-18.02) when high trajectories of rumination occurred in tandem with high trajectories of depression. Multinomial logistic regression showed that male sex (OR 10.54, 95% CI 5.66-19.63), peer victimization (OR 2.25, 95% CI 1.72-2.96), and parental alienation (OR 1.94, 95% CI 1.46-2.57) were key determinants of membership in the highest risk group. Risk for NSSI is markedly increased in adolescents with high longitudinal trajectories of depression and rumination. Reducing exposure to peer victimization, cyber victimization, emotional abuse, parental alienation, and interparental conflict may reduce risk.

Determinants of open membership in brazilian credit unions

Purpose: To identify the determining factors of Open Membership in Brazilian credit unions between 2016 and 2020.&#x0D; Methodology: A logistic regression model was used to estimate these factors. The analysis considered both specific internal variables of the cooperatives (age, asset size, number of members, and affiliation to systems) and variables representing the macroeconomic environment in which these institutions operate (municipal development index, population size in municipalities with service points (PACs) and cooperative headquarters).&#x0D; Results: The main results indicate that a lower population size in the municipalities containing the cooperative headquarters and PACs increases the probability of Open Membership. Additionally, cooperatives with a larger number of members and assets also have a higher likelihood of adopting Open Membership.&#x0D; Contributions of the Study: The study highlights the predominance of cooperatives with Open Membership in smaller and less developed municipalities, reinforcing the role of these cooperatives in social inclusion, reduction of inequalities, and provision of financial services and credit to the population in these locations. Moreover, the results provide valuable insights for policymakers, financial institutions, and cooperatives interested in promoting socio-economic development and equity in underserved communities by the financial system.