AIMS AND OBJECTIVES: To test the validity and reliability of the newly developed Irish Nursing Minimum Data Set for mental health (I-NMDS (MH)) to ensure its clinical usability. BACKGROUND: Internationally, difficulties exist in defining the contribution mental health nursing makes to patient care. Structured information systems, like the Nursing Minimum Data Set, have been developed internationally to gather standardised information to increase the visibility of nursing in the health care system. DESIGN: This study employed a quantitative, longitudinal research design. METHOD: A convenience sample of mental health nurses (n = 184) collected data on the nursing care of patients (n = 367) from care settings attached to 11 hospitals across Ireland. Exploratory factor analysis (EFA), ridit analysis and Cronbach's alpha coefficient were used to establish the construct and discriminative validity and scale score reliability of the I-NMDS (MH). RESULTS: Goodness of Fit scores indicated that the I-NMDS (MH) possesses good construct validity. Alpha coefficients for each factor were above the recommended 0.7 level. Ridit analysis inferred that the I-NMDS (MH) discriminated between elements of nursing care across acute inpatient and community based care settings. CONCLUSIONS: The I-NMDS (MH) possesses a sound theoretical base, has scale score reliability and possesses good discriminative validity. The valid and reliable I-NMDS (MH) is the first NMDS to be developed specifically for mental health. RELEVANCE TO CLINICAL PRACTICE: Data collected using the I-NMDS (MH) will increase the visibility of the contribution mental health nurses make to healthcare delivery. In addition, it will support evidence based practice in mental health to improve further the effectiveness of nursing care in the future.
AIMS AND OBJECTIVES: To test the validity and reliability of the newly developed Irish Nursing Minimum Data Set for mental health (I-NMDS (MH)) to ensure its clinical usability. BACKGROUND: Internationally, difficulties exist in defining the contribution mental health nursing makes to patient care. Structured information systems, like the Nursing Minimum Data Set, have been developed internationally to gather standardised information to increase the visibility of nursing in the health care system. DESIGN: This study employed a quantitative, longitudinal research design. METHOD: A convenience sample of mental health nurses (n = 184) collected data on the nursing care of patients (n = 367) from care settings attached to 11 hospitals across Ireland. Exploratory factor analysis (EFA), ridit analysis and Cronbach's alpha coefficient were used to establish the construct and discriminative validity and scale score reliability of the I-NMDS (MH). RESULTS: Goodness of Fit scores indicated that the I-NMDS (MH) possesses good construct validity. Alpha coefficients for each factor were above the recommended 0.7 level. Ridit analysis inferred that the I-NMDS (MH) discriminated between elements of nursing care across acute inpatient and community based care settings. CONCLUSIONS: The I-NMDS (MH) possesses a sound theoretical base, has scale score reliability and possesses good discriminative validity. The valid and reliable I-NMDS (MH) is the first NMDS to be developed specifically for mental health. RELEVANCE TO CLINICAL PRACTICE: Data collected using the I-NMDS (MH) will increase the visibility of the contribution mental health nurses make to healthcare delivery. In addition, it will support evidence based practice in mental health to improve further the effectiveness of nursing care in the future.