Pedagogical Approaches and Teaching Strategies Used in Nursing Education to Teach Academic Writing: A Scoping Review
Bibliographic record
Abstract
Academic writing is considered an essential skill in post-secondary education in general and in nursing specifically (Mitchell et al., 2020). The importance of writing for future nurses is reflected in the Canadian Association of Schools of Nursing (CASN) National Nursing Education Framework’s standards. This national consensus-based framework outlines the expectations of undergraduate nursing programs and graduates according to the six domains of knowledge, research skills and critical inquiry, nursing practice, communication and collaboration, professionalism, and leadership (CASN, 2022). Academic writing is embedded in each of these domains, as it provides students with a venue to share factual knowledge, demonstrate critical thinking, and make important connections between theory and practice (Naber & Wyatt, 2014). A scoping review of research on the pedagogical approaches and teaching strategies used to teach academic writing in nursing education yielded 12 studies. Findings show a range of approaches with varying degrees of success. Further research is needed on how best to guide and assess students’ writing.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".