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Abstract Introduction In many countries, health-care labour markets are constantly being challenged by an alternation of shortage and oversupply. Avoiding these cyclic variations is a major challenge. In the Netherlands, a workforce planning model has been used in health care for ten years. Case description Since 1970, the Dutch government has explored different approaches to determine the inflow in medical schools. In 2000, a simulation model for health workforce planning was developed to estimate the required and available capacity of health professionals in the Netherlands. In this paper, this model is explained, using the Dutch general practitioners as an example. After the different steps in the model are clarified, it is shown how elements can be added to arrive at different versions of the model, or ‘scenarios’. A comparison is made of the results of different scenarios for different years. In addition, the subsequent stakeholder decision-making process is considered. Discussion and evaluation Discussion of this paper shows that workforce planning in the Netherlands is a complex modelling task, which is sensitive to different developments influencing the balance between supply and demand. It seems plausible that workforce planning has resulted in a balance between supply and demand of general practitioners. Still, it remains important that the modelling process is accepted by the different stakeholders. Besides calculating the balance between supply and demand, there needs to be an agreement between the stakeholders to implement the advised training inflow. The Dutch simulation model was evaluated using six criteria to be met by models suitable for policy objectives. This model meets these criteria, as it is a comprehensive and parsimonious model that can include all relevant factors. Conclusion Over the last decade, health workforce planning in the Netherlands has become an accepted instrument for calculating the required supply of health professionals on a regular basis. One of the strengths of the Dutch model is that it can be used for different types of medical and allied health professionals. A weakness is that the model is not yet fully capable of including substitutions between different medical professions to plan from a skill-mix perspective. Several improvements remain possible.

Introduction: Due to the lack of a theoretically embedded overview of the recent literature on medical career decision-making, this study provides an outline of these dynamics. Since differences in educational routes to the medical degree likely affect career choice dynamics, this study focuses on medical career decision-making in educational systems with a Western European curriculum structure. Methods: A systematic search of electronic databases (Medline, Embase) was conducted from January 2008 to November 2014. A panel of seven independent reviewers performed the data extraction, quality assessment and data synthesis using the Bland–Meurer model of medical specialty choice as a reference. Results: Fifty-seven studies met the inclusion criteria for the review. Factors associated with specialty preference or career choice can be classified in five main categories: (1) medical school characteristics (e.g., curriculum structure), (2) student characteristics (e.g., age, personality), (3) student values (e.g., personal preference), (4) career needs to be satisfied (e.g., expected income, status, and work–life balance), and (5) perception of specialty characteristics (e.g., extracurricular or curricular experiences). Especially career needs and perceptions of specialty characteristics are often associated with medical career decision-making. Conclusion: Our results support that medical career decisions are formed by a matching of perceptions of specialty characteristics with personal needs. However, the process of medical career decision-making is not yet fully understood. Besides identifying possible predictors, future research should focus on detecting interrelations between hypothesized predictors and identify the determinants and interrelations at the various stages of the medical career decision-making process. [ABSTRACT FROM AUTHOR]

Background: Health workforce projections are important instruments to prevent imbalances in the health workforce. For both the tenability and further development of these projections, it is important to evaluate the accuracy of workforce projections. In the Netherlands, health workforce projections have been done since 2000 to support health workforce planning. What is the accuracy of the techniques of these Dutch general practitioner workforce projections? Methods: We backtested the workforce projection model by comparing the ex-post projected number of general practitioners with the observed number of general practitioners between 1998 and 2011. Averages of historical data were used for all elements except for inflow in training. As the required training inflow is the key result of the workforce planning model, and has actually determined past adjustments of training inflow, the accuracy of the model was backtested using the observed training inflow and not an average of historical data to avoid the interference of past policy decisions. The accuracy of projections with different lengths of projection horizon and base period (on which the projections are based) was tested. Results: The workforce projection model underestimated the number of active Dutch general practitioners in most years. The mean absolute percentage errors range from 1.9% to 14.9%, with the projections being more accurate in more recent years. Furthermore, projections with a shorter projection horizon have a higher accuracy than those with a longer horizon. Unexpectedly, projections with a shorter base period have a higher accuracy than those with a longer base period. Conclusions: According to the results of the present study, forecasting the size of the future workforce did not become more difficult between 1998 and 2011, as we originally expected. Furthermore, the projections with a short projection horizon and a short base period are more accurate than projections with a longer projection horizon and base period. We can carefully conclude that health workforce projections can be made with data based on relatively short base periods, although detailed data are still required to monitor and evaluate the health workforce. [ABSTRACT FROM AUTHOR]

Introduction: Medical deserts are increasingly considered problematic and many countries employ a multitude of actions and initiatives to achieve a better distribution of the health workforce (HWF). This study systematically maps research and provides an overview of the definitions/characteristics of medical deserts. It also identifies contributing factors and approaches to mitigate medical deserts., Methods: Embase, MEDLINE, CINAHL, Web of Science Core Collection, Google Scholar and The Cochrane Library were searched from inception to May 2021. Studies reporting primary research on definitions, characteristics, contributing factors and approaches to mitigate medical deserts were included. Two independent reviewers assessed studies for eligibility, extracted data and clustered studies., Results: Two-hundred and forty studies were included (49% Australia/New Zealand, 43% North America, 8% Europe). All used observational designs except for five quasi-experimental studies. Studies provided definitions (n=160), characteristics (n=71), contributing/associated factors (n=113), and approaches to mitigate medical deserts (n=94). Most medical deserts were defined by the density of the population in an area. Contributing/associated factors consisted in sociodemographic/characteristics of HWF (n=70), work-related factors (n=43) and lifestyle conditions (n=34). Approaches focused on training adapted to the scope of rural practice (n=79), HWF distribution (n=3), support and infrastructure (n=6) and innovative models of care (n=7)., Discussion: Our study provides the first scoping review on definitions, characteristics, contributing/associated factors and approaches to mitigate medical deserts. We identified gaps such as the scarcity of longitudinal studies to investigate factors contributing to medical deserts, and interventional studies to evaluate the effectiveness of approaches to mitigate medical deserts.