Bibliographic record
Abstract
REPLY TO DRS TAM AND GOH: REPLICABILITY, TRANSPARENCY, AND CURRENCY IN NURSING REVIEWS Dear Dr Chinn, We wish to thank Drs Tam and Goh for their response to our article and for their work in the area of the science of reviews. The need for replicability, transparency, and currency is important for upholding rigor in nursing reviews. While both of our articles1,2 address the science of reviews, the focus of their article was to examine the time taken between the last search, submission, acceptance, and publication dates of systematic reviews published in nursing journals. In contrast, our article examined the characteristics of nurse-led reviews and was guided by PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.3 Because of the differing foci, we discourage direct comparisons between each article. Instead, we think that both articles can help authors, reviewers, and editors start to ameliorate issues central to the rigor of reviews in nursing. Drs Tam and Goh's suggestion that the use of the PRISMA guidelines to report only systematic reviews and meta-analyses is too narrow of a view and may be detrimental to improving the science of reviews in nursing. The PRISMA guidelines may be used as applicable, with other types of reviews such as integrative reviews and scoping reviews4 to ensure completeness of reporting. At the very least, these guidelines can bring some standardization to reporting the search process among reviews. An increasing number of publishers have accepted PRISMA as a comprehensive reporting guideline since the guidelines were disseminated in 2009.5 Furthermore, some journals have started to encourage or even require authors to complete the PRISMA checklist for relevant reviews. Both of our articles highlight the need for accuracy of reporting in reviews. Explicit documentation of search strategies used in systematic and integrative reviews has a significant role in the quality assessment and reproducibility of reviews. We concluded that transparent search strategies are not consistently documented in nursing reviews. Our 2 articles differ in focus related to search date information provided in a review. As noted in Tam and Goh's response, our findings revealed that several reviews reported end dates of searches as “to present.” Our review was concerned with the impact of this phrase on reproducibility of the search, not of timeliness of search date and publication. We agree that results can certainly become obsolete by the time a paper goes to press. However, thorough reporting of search criteria and methods is crucial for a number of reasons so that other researchers may (1) evaluate the quality of a search (eg, ensure the review was truly systematic and not based on hand-picked articles), and (2) conduct subsequent reviews of the topic at a later date, starting where the prior review left off. Going forward, journal editors and reviewers should encourage authors to thoroughly report methods used. Thank you again for your work in this area and for your interest in our article. —Coleen E. Toronto, PhD, RN, CNE Associate Professor School of Nursing Curry College Milton, Massachusetts —Brenna L. Quinn, PhD, RN, NCSN Assistant Professor Solomont School of Nursing University of Massachusetts Lowell Lowell, Massachusetts —Ruth Remington, PhD, AGPCNP-BC Professor Department of Nursing Framingham State University Framingham, Massachusetts
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.110 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.012 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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; both teacher heads 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".