Pedagogical and teaching strategies used to teach writing to pre-licensure students enrolled in health professional programs: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to map the existing literature on pedagogical approaches and teaching strategies used to teach academic and professional writing to pre-licensure students who are enrolled in health professional programs. INTRODUCTION: Health professional programs are intended to develop competence in both academic and professional writing. Effective academic writing skills prompt critical reflection and engagement with research communities, while professional writing skills are used to document interventions and communicate across health systems. Despite the importance of these 2 forms of writing, there are ongoing concerns that practitioners are entering practice without adequate writing skills. Given these concerns and the importance of writing across health disciplines, there is value in identifying the pedagogical strategies and approaches used in health professional programs to develop writing skills and to transfer such skills from one communicative context to another. INCLUSION CRITERIA: This review will consider research on the pedagogical approaches and teaching strategies used to teach academic and professional writing in pre-licensure health professional programs. METHODS: This review will be conducted in line with the JBI methodology for scoping reviews. The search strategy will aim to locate published literature using MEDLINE (Ovid), Embase, CINAHL with Full-Text (EBSCOhost), ProQuest Nursing and Allied Health (ProQuest), and ERIC (EBSCOhost), along with gray literature (using databases/search engines). Papers published from 2010 onward in English and in French will be included. Extracted data will be reported in tabular format and presented narratively to address each review objective. REVIEW REGISTRATION: Open Science Framework http://osf.io/9raxp.
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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 | 0.097 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".