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Methodological issues in health human resource planning: cataloguing assumptions and controlling for variables in
1School of Nursing, Dalhousie University, Halifax, Nova Scotia, Canada. Gail.Tomblin.Murphy@dal.ca
Summary
Accurate health human resource planning (HHRP) requires understanding statistical model biases. This review explores bias sources and suggests multilevel and small area variation modeling for better nursing workforce predictions.
Area of Science:
- Health Services Research
- Biostatistics
- Nursing Workforce Studies
Background:
- Health Human Resource Planning (HHRP) models are crucial for forecasting nursing requirements.
- Existing HHRP models may be subject to statistical biases, necessitating a deeper understanding of their sources.
- Accurate and robust formulae are essential for reliable workforce planning.
Purpose of the Study:
- To review and discuss literature on sources of bias in needs-based HHRP research for nursing.
- To explore key issues including assumption testing, ecological and atomistic fallacies, and the relationship between health needs and healthcare.
- To examine alternatives to aggregate analysis for assessing health needs and nursing service utilization.
Main Methods:
- Literature review and discussion of statistical modeling in HHRP.
- Analysis of assumption testing in health workforce models.
- Exploration of ecological and atomistic fallacies in health services research.
- Examination of the relationship between health needs and healthcare utilization.
- Evaluation of alternative analytical approaches to aggregate data.
Main Results:
- Multilevel modeling is effective for simulating individual and ecological factors in workforce planning.
- Small area variation modeling shows potential for analyzing health needs and nursing service utilization.
- Addressing biases in statistical models is critical for accurate nursing requirement projections.
Conclusions:
- Multilevel modeling offers a valuable approach for complex simulation analyses in HHRP.
- Small area variation modeling presents a promising avenue for understanding the nuanced relationship between health needs and nursing service use.
- Improved understanding and mitigation of statistical biases are vital for effective nursing workforce planning.
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