11 research outputs found
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Evaluating an organization-wide disparity reduction program: Understanding what works for whom and why
Background: Disparity-reduction programs have been shown to vary in the degree to which they achieve their goal; yet the causes of these variations is rarely studied. We investigated a broad-scale program in Israel’s largest health plan, aimed at reducing disparities in socially disadvantaged groups using a composite measure of seven health and health care indicators. Methods: A realistic evaluation was conducted to evaluate the program in 26 clinics and their associated managerial levels. First, we performed interviews with key stakeholders and an ethnographic observation of a regional meeting to derive the underlying program theory. Next, semi-structured interviews with 109 clinic teams, subregional headquarters, and regional headquarters personnel were conducted. Social network analysis was performed to derive measures of team interrelations. Perceived team effectiveness (TE) and clinic characteristics were assessed to elicit contextual characteristics. Interventions implemented by clinics were identified from interviews and coded according to the mechanisms each clinic employed. Assessment of each clinic’s performance on the seven-indicator composite measure was conducted at baseline and after 3 years. Finally, we reviewed different context-mechanism-outcome (CMO) configurations to understand what works to reduce disparity, and under what circumstances. Results: Clinics’ inner contextual characteristics varied in both network density and perceived TE. Successful CMO configurations included 1) highly dense clinic teams having high perceived TE, only a small gap to minimize, and employing a wide range of interventions; (2) clinics with a large gap to minimize with high clinic density and high perceived TE, focusing efforts on tailoring services to their enrollees; and (3) clinics having medium to low density and perceived TE, and strong middle-management support. Conclusions: Clinics that achieved disparity reduction had high clinic density, close ties with middle management, and tailored interventions to the unique needs of the populations they serve.</p
Journal officiel de la République française. Lois et décrets
03 février 18861886/02/03 (A18,N33).Appartient à l’ensemble documentaire : MAEDIGen0Appartient à l’ensemble documentaire : MAEDI008Appartient à l’ensemble documentaire : MAEDI012Appartient à l’ensemble documentaire : MAEDI006Appartient à l’ensemble documentaire : MAEDI00
Context: Characteristics of target clinics.
<p>Context: Characteristics of target clinics.</p
Context-mechanism-outcome configurations of target clinics.
<p>Context-mechanism-outcome configurations of target clinics.</p
Implementation of QI program by target clinics.
<p>Implementation of QI program by target clinics.</p
Outline of organizational structure and data collection strategy.
<p>d = clinic director, a physician; n = nursing director; a = administrative head; p = pharmacist.</p
Outcomes: Change in QUIDS score of target clinics.
<p>Outcomes: Change in QUIDS score of target clinics.</p
Health equity and COVID-19 : global perspectives
The COVID-19 is disproportionally affecting the poor, minorities and a broad range of vulnerable populations, due to its inequitable spread in areas of dense population and limited mitigation capacity due to high prevalence of chronic conditions or poor access to high quality public health and medical care. Moreover, the collateral effects of the pandemic due to the global economic downturn, and social isolation and movement restriction measures, are unequally affecting those in the lowest power strata of societies. To address the challenges to health equity and describe some of the approaches taken by governments and local organizations, we have compiled 13 country case studies from various regions around the world: China, Brazil, Thailand, Sub Saharan Africa, Nicaragua, Armenia, India, Guatemala, United States of America (USA), Israel, Australia, Colombia, and Belgium. This compilation is by no-means representative or all inclusive, and we encourage researchers to continue advancing global knowledge on COVID-19 health equity related issues, through rigorous research and generation of a strong evidence base of new empirical studies in this field