Process evaluation to explore internal and external validity of the "Act in Case of Depression" care program in...

A multidisciplinary, evidence-based care program to improve the management of depression in nursing home residents was implemented and tested using a stepped-wedge design in 23 nursing homes (NHs): "Act in case of Depression" (AiD).

Before effect analyses, to evaluate AiD process data on sampling quality (recruitment and randomization, reach) and intervention quality (relevance and feasibility, extent to which AiD was performed), which can be used for understanding internal and external validity. In this article, a model is presented that divides process evaluation data into first- and second-order process data.

Qualitative and quantitative data based on personal files of residents, interviews of nursing home professionals, and a research database were analyzed according to the following process evaluation components: sampling quality and intervention quality.

Nursing home.

The pattern of residents' informed consent rates differed for dementia special care units and somatic units during the study. The nursing home staff was satisfied with the AiD program and reported that the program was feasible and relevant. With the exception of the first screening step (nursing staff members using a short observer-based depression scale), AiD components were not performed fully by NH staff as prescribed in the AiD protocol.

Although NH staff found the program relevant and feasible and was satisfied with the program content, individual AiD components may have different feasibility. The results on sampling quality implied that statistical analyses of AiD effectiveness should account for the type of unit, whereas the findings on intervention quality implied that, next to the type of unit, analyses should account for the extent to which individual AiD program components were performed. In general, our first-order process data evaluation confirmed internal and external validity of the AiD trial, and this evaluation enabled further statistical fine tuning. The importance of evaluating the first-order process data before executing statistical effect analyses is thus underlined.