Health Care Aides (HCAs) provide up to 80% of the direct care to older Canadians living in long term care facilities, or in their homes. They are an understudied workforce, and calls for health human resources strategies relating to these workers are, we feel, precipitous. First, we need a better understanding of the nature and scope of their work, and of the factors that shape it. Here, we discuss the evolving role of HCAs and the factors that impact how and where they work. The work of HCAs includes role-required behaviors, an increasing array of delegated acts, and extra-role behaviors like emotional support. Role boundaries, particularly instances where some workers over-invest in care beyond expected levels, are identified as one of the biggest concerns among employers of HCAs in the current cost-containment environment. A number of factors significantly impact what these workers do and where they work, including market-level differences, job mobility, and work structure. In Canada, entry into this ‘profession’ is increasingly constrained to the Home and Community Care sector, while market-level and work structure differences constrain job mobility to transitions of only the most experienced workers, to the long-term care sector. We note that this is in direct opposition to recent policy initiatives designed to encourage aging at home. Work structure influences what these workers do, and how they work; many HCAs work for three or four different agencies in order to sustain themselves and their families. Expectations with regard to HCA preparation have changed over the past decade in Canada, and training is emerging as a high priority health human resource issue. An increasing emphasis on improving quality of care and measuring performance, and on integrated team-based care delivery, has considerable implications for worker training. New models of care delivery foreshadow a need for management and leadership expertise - these workers have not historically been prepared for leadership roles. We conclude with a brief discussion of the next steps necessary to generating evidence necessary to informing a health human resource strategy relating to the provision of care to older Canadians.

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This article provides an overview and application of Q-methodology for nursing researchers, with an illustration of its appropriate usage. Q-methodology has been identified as a method for the analysis of subjective viewpoints and has the strengths of both qualitative and quantitative methods. It shares with qualitative methodologies the aim of exploring subjectivity; however, statistical techniques are used to reveal the structure of views. This article describes the use of Q-methodology to examine subjectivity systematically, revealing connections between accounts that other techniques may overlook. An example from the literature is presented. Q-methodology is useful in qualitative nursing research concerned with the exploration and comparison of subjectivity and attitudes. It can be used to effectively identify attitudes, perceptions, feelings, and values as well as explore life experiences such as stress, self-esteem, body image, and satisfaction.

This article is a review of the approaches published between 1996 and 1999 that have been used to forecast human resource requirements for nursing. Much of the work to date generally does not consider the complex factors that influence health human resources (HHR). They also do not consider the effect of HHR decisions on population health, provider outcomes such as stress, and the cost of a decision made. Supply and demand approaches have dominated. Forecasting is limited, too, by the availability of reliable and valid data bases for examining supply and use of nursing personnel across sectors. Three models--needs based, utilization based, and effective demand based--provide substantially different estimates of future HHR need. The methods of analysis employed for forecasting range from descriptive to predictive and are borrowed from demography, epidemiology, economics, and industrial engineering. Simulation models offer the most promise for the future. The forecasting methods described have demonstrated their accuracy and usefulness for specific situations, but none has proven accurate for long-term forecasting or for estimating needs for large geographical areas or populations.