The Irish nursing minimum data set for mental health – a valid and reliable tool for the collection of standardised nursing data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".