How to do (or not to do)… Measuring health worker motivation in surveys in low- and middle-income countries

Abstract

A health system's ability to deliver quality health care depends on the availability of motivated health workers, which are insufficient in many low income settings. Increasing policy and researcher attention is directed towards understanding what drives health worker motivation and how different policy interventions affect motivation, as motivation is key to performance and quality of care outcomes. As a result, there is growing interest among researchers in measuring motivation within health worker surveys. However, there is currently limited guidance on how to conceptualize and approach measurement and how to validate or analyse motivation data collected from health worker surveys, resulting in inconsistent and sometimes poor quality measures. This paper begins by discussing how motivation can be conceptualized, then sets out the steps in developing questions to measure motivation within health worker surveys and in ensuring data quality through validity and reliability tests. The paper also discusses analysis of the resulting motivation measure/s. This paper aims to promote high quality research that will generate policy relevant and useful evidence.

Keywords: Motivation; analysis; health worker; measurement scale.

Figure 1.

Conceptualizing motivation. This figure is adapted from Franco et al. 2002 to convey the determinants and outcomes of motivation and the dimensions of motivation within a multi-dimensional framework. M1–M5 are factors, which represent different dimensions of motivation. Within self-determination theory, these could be the following: M1—motivation factor 1 (e.g. integrated motivation), M2—motivation factor 2 (e.g. identified motivation), M3—motivation factor 3 (e.g. introjected motivation), M4—motivation factor 4 (e.g. external regulation), and M5—motivation factor 5 (amotivation) Tremblay et al. 2009.

Figure 2.

Scree plot for survey data collected in Tanzania. Based on visual inspection alone, 5 factors appear to be the turning point after which the plot levels off (though it does so again at 8). However, using the Kaiser criterion (retaining factors with an eigen value of 1 or more), 3 factors would be retained

Figure 3.

Spider diagram showing changes in composite scores over time. In Tanzania, management and supervision, fairness, transparency, organisation, the work environment, financial aspects of the job, and intrinsic factors (commitment, conscientiousness and self-efficacy) were identified as potential dimensions of motivation. Conscientiousness, commitment to the job, and management and supervision scored highest.

Figure 4.

Illustration of the use of structural equation models for confirmatory factor analysis and the analysis of motivation determinants