Audit of curriculum content to assess the integration of CASN informatics competencies in a BScN Program
Bibliographic record
Abstract
In 2012 the Canadian Association of Schools of Nursing (CASN) published “Nursing Informatics: Entry-to-practice competencies for Registered Nurses.” These nursing informatics (NI) competencies provide a clear description of the NI competencies that student nurses should meet upon graduation. The use of evidence-informed practice and information technology is increasingly common in clinical practice settings and employers expect that nurses will enter practice with competency in the use of information and communication technology (ICT). Despite the proliferation of technology there is a need to strengthen the capacity of graduating nurses to manage information and to use health care specific information systems for the planning and evaluation of nursing care. Nurse educators must take an active role to ensure that students have the learning opportunities to develop these competencies. This project is an important first step toward integration of the NI competencies in a curriculum. Iwasiw and Goldenberg’s (2009) context-relevant curriculum development model was used as a guide. An audit tool was adapted and a pilot audit was completed to assess the extent to which NI entry to practice competencies (CASN, 2012) were covered in five of the courses in St. Francis Xavier University (STFX) Bachelor of Science in Nursing (BScN) curriculum. All participants in the project expressed interest and enthusiasm for strengthening the integration of informatics competencies in nursing courses across the curriculum. Key gaps tend to be in the area of nursing specific informatics competency and involvement in the use and development of health information systems. Continuation of the audit of nursing courses for NI competencies and a strategic approach to strengthening integration of NI competencies are recommended.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".