Interpretability of the Canadian Nurse Informatics Competency Assessment Scale Among Fourth-Year Nursing Students
Bibliographic record
Abstract
Nursing informatics merges nursing practice, its information and knowledge, with information communication technologies to improve patient care. Uptake of informatics competencies can be measured using self-perceived assessment scales. A scale for measuring Canadian nursing informatics has been recently developed from national competency indicators. In order to examine its wording and interpretability, cognitive interviewing was conducted with eight fourth-year nursing students as they completed the Canadian Nurse Informatics Competency Assessment Scale. Findings revealed issues related to misinterpreted survey items, items seen as “difficult” to answer, and specific words and phrases not recognized or misinterpreted. Furthermore, design flaws such technology-related jargon, wording ambiguity, or double-barrelled questions were revealed. Correspondingly, specific item and response re-wording revisions have been recommended to improve wording, interpretability and scale validity. Improving this scale may contribute to nursing informatics assessment and uptake in Canada which may be timely and strategic given that nursing informatics preparedness in Canada lags.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".