Teaching professional writing in prelicensure health professional education programs: a scoping review
Bibliographic record
Abstract
OBJECTIVE: This review aimed to map the literature on teaching strategies used to teach professional writing to prelicensure students enrolled in health professional programs. INTRODUCTION: Health education programs must teach students how to practice professional writing as it is a fundamental skill for effective communication in health care. Professional writing is crucial for ensuring continuity of care, promoting patient safety, and meeting regulatory and institutional standards. Understanding the teaching strategies used to develop professional writing skills is important because it helps educators identify the most effective methods for preparing students for practice. ELIGIBILITY CRITERIA: This review considered studies on teaching strategies used by faculty at any academic institution, in any country, to teach professional writing in prelicensure health professional programs. Disciplines such as medicine, nursing, occupational therapy, pharmacy, dentistry, and veterinary medicine were included. Strategies included any intentional activity (in-person or virtual) aimed at developing students' professional writing. Professional writing included writing for the purpose of recording assessments or interventions, conveying information to a care team, communicating with patients, or demonstrating compliance with professional or institutional policies or practices. METHODS: This review followed the JBI methodology for scoping reviews. Published literature was located in MEDLINE (Ovid), Embase (Ovid), CINAHL with Full Text (EBSCOhost), ProQuest Nursing and Allied Health (ProQuest), and ERIC (EBSCOhost). A search for unpublished research reports was conducted in ProQuest Dissertations and Theses, Open Access Theses and Dissertations (OATD), and OAIster (WorldCat). The reference lists of all included studies were manually back-searched for additional studies, and Google Scholar and Web of Science Core Collection were used for forward citation tracking to identify further studies. Search results were limited from 2010 to the present, and only reports written in English and French were eligible. Data were extracted from studies that met the eligibility criteria by 2 independent reviewers. Data are presented in tabular format to address findings related to the review objectives. RESULTS: Thirty-three studies from 7 countries, published between 2010 and 2025, were included. All studies examined at least 1 teaching strategy, and included 5 disciplines: nursing, medicine, pharmacy, dentistry, and veterinary medicine. Three additional studies focused on interprofessional education. A variety of strategies was used to teach professional writing, with the most common being didactic methods such as lectures, as well as checklists. These strategies included group and individualized modes of delivery and targeted a range of professional writing types, including assessments, discharge summaries, prescriptions, and patient education materials. CONCLUSION: Most of the studies included in this review were published within the last 5 years, highlighting the growing recognition of the need to prepare future health care graduates to write professionally. This review reveals a gap in understanding of the most effective methods for teaching professional writing in prelicensure programs. Future research should identify the best teaching strategies and develop standardized evaluation metrics to ensure that health profession students are fully equipped to meet the writing demands of clinical practice. REVIEW REGISTRATION: OSF https://osf.io/nveqr/.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".