Addressing Nursing Scholarship: A Framework for Currency and Number of References
Bibliographic record
Abstract
BACKGROUND: Timeliness and number of references in written work is often a topic of controversy. Decisions about choice of references become complex when there is little recent published information or a great deal of important historical work on a topic. PURPOSE: The study aim was to develop a framework to guide authors to determine the number and currency of references to support their writing. METHODS: This study used a descriptive design with three steps: review of journal author information for guidance about reference currency (n = 247); correspondence with journal editors (n = 27); and a survey of nurse educators (n = 44) regarding currency and number of references in written assignments. RESULTS: Findings affirmed that recent literature is vital for nursing scholarship. Numerical guidelines offered were not based on identifiable consensus or rationale. Historical perspectives published over 5 or 10 years earlier are valued, even sometimes required. For a clinical paper, citation of the most current literature is viewed by editors and educators as essential, and may suffice. CONCLUSION: Based on the findings of this study and our search of the literature, we developed three decision making algorithms for searching the literature and selecting references by currency and number.
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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 | 0.336 | 0.612 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.069 | 0.044 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.006 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".