Make writing a daily habit: An evaluation of an educational intervention to improve writing self-efficacy among DNP students
Bibliographic record
Abstract
Background and objective: Doctorate of Nursing Practice (DNP) students are trained to integrate both clinical care and evidence-based research in order to bring together science with application. However, the educational pathways in DNP programs can be problematic, especially with regards to scholarly writing. While several interventions have been utilized for DNP students, the results show that the intervention(s) used should be tailored to the specific student body being served. However, limited evidence exists regarding the effectiveness of tailored interventions on improving central concepts such as writing self-efficacy. Given these differences in the design and delivery of the DNP curricula, we created a tailored educational-writing curriculum for new DNP students at a medium-sized academic medical center in a Southern state.Methods: We assessed changes in writing self-efficacy over the three measurement intervals using linear mixed effects modeling to account for within-student clustering of writing self-efficacy scores over time.Results: Baseline scores of writing self-efficacy improved immediately after the workshop (Timepoint 2 – immediate post-test) and a full semester later (Timepoint 3 – semester post-test). However, we observed no statistically significant difference between Timepoint 2 (immediate post-test) and Timepoint 3 (semester post-test).Conclusions: We saw a significant benefit in writing self-efficacy among incoming DNP students from baseline scores. The tailored format and integration of real-life anecdotal feedback from faculty may have been fundamental to creating an increase in writing self-efficacy among students—a concept foundational to student, and possibly professional, nursing success.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".