In the United States, as in many other parts of the world, the prevalence of overweight/obesity is at epidemic proportions in the adult population and even higher among Veterans. To address the high prevalence of overweight/obesity among Veterans, the MOVE! weight management program was disseminated nationally to Veteran Affairs (VA) medical centers. The objective of this paper is two-fold: to describe factors that explain the wide variation in implementation of MOVE!; and to illustrate, step-by-step, how to apply a theory-based framework using qualitative data.
Dissemination of the MOVE! weight management program in a network of 155 medical centers and 872 community-based outpatient clinics made this the largest and most comprehensive dissemination of a weight management program in the U.S. [11, 15]. In the first year of the program, only about 8 per 1000 Veterans who were candidates for MOVE! (body mass index more than 30 kg/m2, or between 25 to 30 kg/m2 with one or more obesity-related chronic health conditions, e.g., hyperlipidemia [11]) actually participated in the program. In the second year of the program, local facilities varied widely in the number of candidate Veterans who participated in MOVE!, from no participants at many facilities to a high of 37 participants per 1,000 MOVE! candidates [15]. MOVE! cannot help Veterans if it is not implemented as designed.
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Relative advantage was a strongly distinguishing construct. The two high implementation facilities both had strongly positive perceptions about the advantages of MOVE!. Even the facility with the most well-developed weight management program welcomed MOVE! because of the additional visibility and attention it brought to their weight management program:
We evaluated implementation of a weight management program that was disseminated to all VA facilities in 2007. Using a systematic approach based on a qualitative, consensus-based rating process, guided by the CFIR, we found that twelve constructs manifested more positively (ten strongly differentiated, two weakly differentiated) in the high implementation compared to low implementation facilities. Additional file 3 provides recommended actions to improve the influence of each of the differentiating constructs based on our findings.
We obtained significant differences according to the geographical location of the country, with European and American countries being less efficient than Asian and African countries. Likewise, we can affirm that greater freedom of expression, a higher median age and an unstable economy and labor market reduce efficiency. However, female leadership of the government and greater compliance with the rule of law offer more efficient management, as do countries that derive more revenues from tourism.
To calculate the levels of efficiency with DEA, the inputs and outputs must be selected, and these are will be determined by the research objective. In our case, to determine the efficiency of pandemic management, the inputs refer to the resources available to manage a health crisis of these characteristics, and the outputs to its direct consequences. Thus, following the most recent literature analyzing efficiency in the health sector [17, 29], the selected inputs are the available physicians (Physicians) and nurses (Nurses), the number of hospital beds (Hospital beds) and the current expenditure on health care (Health expenditure), while the number of people infected by Covid-19 (Cases confirmed) and the number of deaths (Death rate) form the outputs. Table 1 explains each input and output in more detail, as well as the sources of information and descriptive statistics.
If the countries of Europe and the Americas have more resources (inputs) for pandemic management (see Table 3), their results should be more favorable. If this is not the case, we confirm the inefficiency in the management of health resources by these countries. These data could be explained by the capacity and information acquired in recent decades in Asian countries, as a result of having effectively combated similar viruses (SARS and MERS-CoV). This is also the case in African countries, where they coexist with more uncontrolled diseases (Ebola and Malaria) that bestow on society a greater awareness of the extraordinary measures of health protection. Although the average efficiency of Africa is higher than that of Europe or Americas, some countries with economic and tourism solvency are below the average of the latter, as is the case of South Africa or Tunisia (0.7352 and 0.8093, respectively).
During the first weeks of the pandemic, political leaders in some countries such as the United States, Brazil and the United Kingdom denied the extent and consequences of the virus, implementing measures that favoured its spread. Thus, the efficiency levels obtained in these countries (0.8084; 0.7215; 0.6809, respectively) do not correspond to their economic, political and social characteristics. On the opposite side we find the Asian country where the virus originated (China), or those with more experience in pandemic management for having solved similar situations (South Korea), with very high levels of efficiency (0.9993; 0.9841, respectively). On the other hand, the arrival of Covid-19 in European countries such as Germany, France and Spain tested the response capacity of their governments, which tried to improve on the management carried out by the first western country affected by the pandemic (Italy); however, the results confirm that more and better can be done (0.7153; 0.7248; 0.7567; 0.6018, respectively).
Countries that receive more revenue from tourism activities (Tourism) improve their efficiency. We understand that these countries strive to offer a good image that is capable of continuing to attract a high number of visitors, so proper management of the pandemic will improve their prestige and maintain the economic activity associated with this sector.
To this end, we calculated the levels of efficiency in the management of health resources and estimated the impact of Territory, Politics and Governance characteristics controlled by demographic and economic variables of the country. We use DEA as the most appropriate technique to obtain efficiency with undesirable outputs (Cases confirmed and Death rate). We found that the countries that use more resources in the health system obtain worse results in the management of the pandemic. In particular, European and American countries are less efficient than Asian and African countries.
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Throughout the world, countries are experiencing shortages of health care workers. Policy-makers and system managers have developed a range of methods and initiatives to optimise the available workforce and achieve the right number and mix of personnel needed to provide high-quality care. Our literature review found that such initiatives often focus more on staff types than on staff members' skills and the effective use of those skills. Our review describes evidence about the benefits and pitfalls of current approaches to human resources optimisation in health care. We conclude that in order to use human resources most effectively, health care organisations must consider a more systemic approach - one that accounts for factors beyond narrowly defined human resources management practices and includes organisational and institutional conditions.
Despite conflicting findings and the need for further research, a number of studies and systematic reviews suggest that a richer staff-mix may be associated with better outcomes and fewer adverse events for patients. The evidence, however, is highly limited by practical limitations and methodological shortcomings. While many studies have reported positive impacts from enriching staff-mix, they do not offer clear guidance about ideal thresholds in terms of personnel/patient ratios or the proportion of different categories of staff members on teams. More fundamentally, the staff-mix perspective that emphasises numbers and types of personnel gives less attention to the conditions that determine how staff members' skills are used. Despite the rhetorical use of 'skill mix' to describe the different options for deploying health care personnel, the focus is, in reality, not on skill but on grades, educational qualifications, job titles and duration of experience that are, at best, proxies for skill levels. An effective system of HR optimisation cannot, however, be restricted to the numbers and types of personnel available. Such a system must also ensure that personnel work to their full potential. Doing so requires a more dynamic approach to skill management that goes beyond the mix of available staff members.
Second, work expansion, even in a vertical direction, is not always synonymous with job enrichment or role enhancement. In the absence of an explicit professionalization project, HR management strategies designed to expand practice scopes may undermine professionals' distinctive work domains because they blur role boundaries and make the work of one profession indistinguishable from that of others. Lack of clarity about professional practice means that, in fulfilling useful, flexible, and cost-effective new roles, individuals may serve managerial, economic, and patient interests, but their roles may remain limited and lack any obvious benefits for the development of their professions. Some analysts have even suggested that the skill-mix changes that have recently gained popularity (e.g., addition of new functions to nurses' roles) are nothing more than revamped versions of rationalisation programmes, undertakings that exposed workers to a potent mix of resource constraints, heavy workloads, significant role changes, and pressures to develop a broader range of skills [105, 106]. These increased pressures to develop new skills and reach higher educational standards may be counter-productive if they demotivate workers who feel they must take on additional work without reciprocal support [107]. 2ff7e9595c
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