Constructing content validity of clinical nurse specialist core competencies: exploratory sequential mixed‐method study
Bibliographic record
Abstract
RATIONAL: The demand to increase nursing competence is brought on by the requirement of safe, accessible and more effective use of healthcare provider expertise. Clinical nurse specialist competency development dates back to the late 20th century; however, an examination of the literature reveals a lack of research and discussion to support the competency development. OBJECTIVES: To describe the formulation and validation process of the clinical nurse specialist core competencies. DESIGN: Exploratory sequential mixed-method design. METHODS: This mixed-method study, conducted between 2013 and 2017 in Finland, involved four phases: I) a Policy Delphi study (n = 25, n = 22, n = 19); II) cross-mapping of preliminary competency criteria against international competency sets; III) content validity study of expanded competency criteria (n = 7, n = 10); and IV) verification of competency criteria with practicing CNSs (n = 16). Data were analysed by both qualitative and quantitative analysis methods. RESULTS: Seventy-four preliminary clinical nurse competency criteria were formulated in the first phase of the study. Through cross-mapping the competencies against the US and Canadian clinical nurse specialist competency sets, they were further concised to 61 criteria. The examination of Content Validity Indexes and experts' comments led to the clarification and consequent inclusion of 50 criteria to the final scale, with Scale Content Validity Index Average of 0.94. The competency criteria were evaluated to be a solid set with potential to clarify and uniform the clinical nurse specialist roles. CONCLUSIONS: Through a rigorous research process, validated clinical nurse specialist competency criteria were formed with a high Scale Content Validity Index Average. The results allude to the potential of formulating international competency criteria to support global role clarity and understanding. However, further research is needed to validate the content and construct of the formulated competencies with a larger population across countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".