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管理国家放射肿瘤学家劳动力:劳动力规划模型。

Managing a national radiation oncologist workforce: a workforce planning model.

机构信息

Division of Radiation Oncology, Cancer Care Program, NL, Canada.

出版信息

Radiother Oncol. 2012 Apr;103(1):123-9. doi: 10.1016/j.radonc.2011.12.025. Epub 2012 Jan 31.

Abstract

PURPOSE

The specialty of radiation oncology has experienced significant workforce planning challenges in many countries. Our purpose was to develop and validate a workforce-planning model that would forecast the balance between supply of, and demand for, radiation oncologists in Canada over a minimum 10-year time frame, to identify the model parameters that most influenced this balance, and to suggest how this model may be applicable to other countries.

METHODS

A forward calculation model was created and populated with data obtained from national sources. Validation was confirmed using a historical prospective approach.

RESULTS

Under baseline assumptions, the model predicts a short-term surplus of RO trainees followed by a projected deficit in 2020. Sensitivity analyses showed that access to radiotherapy (proportion of incident cases referred), individual RO workload, average age of retirement and resident training intake most influenced balance of supply and demand. Within plausible ranges of these parameters, substantial shortages or excess of graduates is possible, underscoring the need for ongoing monitoring.

CONCLUSIONS

Workforce planning in radiation oncology is possible using a projection calculation model based on current system characteristics and modifiable parameters that influence projections. The workload projections should inform policy decision making regarding growth of the specialty and training program resident intake required to meet oncology health services needs. The methods used are applicable to workforce planning for radiation oncology in other countries and for other comparable medical specialties.

摘要

目的

在许多国家,放射肿瘤学专业都面临着重大的劳动力规划挑战。我们的目的是开发和验证一种劳动力规划模型,该模型将预测加拿大在至少 10 年内放射肿瘤学家的供应与需求之间的平衡,并确定对这种平衡影响最大的模型参数,并提出该模型如何适用于其他国家。

方法

创建了一个正向计算模型,并使用从国家来源获得的数据进行填充。使用历史前瞻性方法确认了验证。

结果

根据基本假设,该模型预测 RO 受训者短期内会出现过剩,然后预计 2020 年将出现短缺。敏感性分析表明,放疗的可及性(新发病例的转诊比例)、个体 RO 工作量、平均退休年龄和住院医师培训入学人数对供需平衡的影响最大。在这些参数的合理范围内,可能会出现大量毕业生短缺或过剩,这突出表明需要进行持续监测。

结论

使用基于当前系统特征和影响预测的可修改参数的预测计算模型,可以进行放射肿瘤学的劳动力规划。工作量预测应告知有关专业发展和培训计划住院医师入学人数的政策决策,以满足肿瘤卫生服务需求。所使用的方法适用于其他国家和其他可比医学专业的放射肿瘤学劳动力规划。

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