Healthcare professionals' views on the mutual consistency of the Finnish Classification of Nursing Interventions and the Oulu Patient Classification.

BACKGROUND

Various classification schemes have been used for clinical and administrative purposes, but their concepts have seldom been cross-mapped. Cross-mapping is a formal method that can be used for examining existing classifications' validity.

AIM

The aim was to cross-map two nursing classifications - the Finnish Classification of Nursing Interventions (FiCNI, version 3.0), developed for structured nursing documentation, and the Oulu Patient Classification (OPCq), created for daily assessment of nursing intensity - and evaluate their mutual consistency. The objective was to obtain information on the content equivalence of the two classifications and the consistency of the concepts used, to inform further development of both.

METHODS

The Delphi method was utilised, with a panel of experts that included terminology developers, researchers, teachers and nurses (n = 16). Four Delphi rounds were required. In these, panellists selected the most relevant content from the OPCq subareas (n = 6) for each FiCNI main category and subcategory (n = 307). In cases of doubt, respondents were asked to justify their choices.

RESULTS

Response rates ranged from 68.8 to 93.8% in the Delphi rounds. After three rounds, 81.4% of the FiCNI categories were cross-mapped with sufficient consensus. A fourth round was needed for 57 FiCNI categories (18.6%). Most FiCNI categories (29.3%) were cross-mapped to the OPCq's subarea 2. Deficiencies in the concepts' clarity and in the OPCq's content areas complicated the cross-mapping. The most unambiguous categories of the FiCNI were found in the respiratory, cardiac and activity components. The components showing greatest ambiguity were skin integrity, mental balance and safety.

CONCLUSION

The content and concepts used in both classifications need further development. The cross-mapping results can be utilised for developing reuse of structured nursing data in assessment of nursing intensity and in decision-making in human-resources planning.