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 \nInformatics: Entry-to-practice competencies for Registered Nurses.” These nursing \ninformatics (NI) competencies provide a clear description of the NI competencies that \nstudent nurses should meet upon graduation. The use of evidence-informed practice and \ninformation technology is increasingly common in clinical practice settings and \nemployers expect that nurses will enter practice with competency in the use of \ninformation and communication technology (ICT). Despite the proliferation of \ntechnology there is a need to strengthen the capacity of graduating nurses to manage \ninformation and to use health care specific information systems for the planning and \nevaluation of nursing care. Nurse educators must take an active role to ensure that \nstudents have the learning opportunities to develop these competencies. This project is an \nimportant first step toward integration of the NI competencies in a curriculum. Iwasiw \nand Goldenberg’s (2009) context-relevant curriculum development model was used as a \nguide. An audit tool was adapted and a pilot audit was completed to assess the extent to \nwhich NI entry to practice competencies (CASN, 2012) were covered in five of the \ncourses in St. Francis Xavier University (STFX) Bachelor of Science in Nursing (BScN) \ncurriculum. All participants in the project expressed interest and enthusiasm for \nstrengthening the integration of informatics competencies in nursing courses across the \ncurriculum. Key gaps tend to be in the area of nursing specific informatics competency \nand involvement in the use and development of health information systems. Continuation \nof the audit of nursing courses for NI competencies and a strategic approach to \nstrengthening integration of NI competencies are recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".