Thank You, DCCN 2023 Peer Reviewers
Bibliographic record
Abstract
Once again, we offer our sincere gratitude to our dedicated peer reviewers who validate the true measure of a professional. They contribute to our knowledge-based profession in a unique way. Peer reviewers donate their time and expertise as unpaid volunteers who balance work-life responsibilities exceptionally well. This group of experts guide our authors and helps our journal maintain a level of excellence. Michael D. Aldridge, MSN, RN, CNS, CNE, Austin, Texas Katherine Alford, MSN, RN, CCRN, PCCN, San Antonio, Texas Linda Baas, PhD, RN, ACNP, CCNS, Harrison, Ohio Stefano Bambi, PhD, RN, CCN, Florence, Italy Cathy Bays, PhD, RN, Louisville, Kentucky Ashley Blatchley, MSN, RN, CNML, Portland, Oregon Leanne Boehm, PhD, RN, ACNS-BC, Nashville, Tennessee Shannon Johnson Bortolotto, MS, RN, Aurora, Colorado Helene Bowen-Brady, DNP, MEd, RN-BC, NEA-BA, Walpole, Massachusetts Emmanuele Buccione, MScN, Pescara, Italy Melissa Burton, RN, CCRN, Boston, Massachusetts Glen Carlson, MSN, RN, Kalamazoo, Michigan Rose Constantino, PhD, JD, RN, FAAN, FACFE, Pittsburgh, Pennsylvania Kathleen Costello, BSN, RN, Boston, Massachusetts Jeni Colarusso, BSN, RN, CCRN-K, Salt Lake City, Utah Rhonda Cornell, DNP, APRN, CNP, Mankato, MN Janet T. Crimlisk, DNP, RNCS, NP-C, Boston, Massachusetts Sherill Cronin, PhD, RN, BC, Louisville, Kentucky Kimberly Curtin, DNP, APRN, Houston, TX Brigitte S. Cypress, EdD, RN, CCRN, Pocono Summit, Pennsylvania Jenna Davis, PhD, RNC-NIC, York, Pennsylvania Julianne Evers, DNP, RN, APRN, AGACNP-BC, Louisville, Kentucky Anna Christine Fisk, PhD, RN, Boston, Massachusetts Kathleen Ahern Gould, PhD, RN, Duxbury, Massachusetts Angelica Nicolina Ferrazzi, DNP, MSN, RN-BC, CMSRN, Washington, DC Judy M. Hayes, MSN, RN, NEA-BC, Boston, Massachusetts Kathleen Haubrich, PhD, RN, Hamilton, Ohio Elizabeth K. Herron, PhD, RN, CNE, Harrisonburg, Virginia Cheryl Hines, EdD, MSN, CRNA, Tuscaloosa, Alabama Rosemary Hoffman, PhD, RN, Pittsburgh, Pennsylvania Bonnie Holaday, DNS, RN, FAAN, Clemson, South Carolina Susan Hurst, MSN, RN, CCRN, CNRN, Phoenix, Arizona Jen Hershey, DEd, MSN, RN, CNE, Lancaster, Pennsylvania Chad Johnson, MSN, RN, Thunder Bay, Ontario, Canada Melissa Bailey Johnson, MSN, RN, Philadelphia, Pennsylvania Linda Jean Josephson, MS, RN, Worcester, Massachusetts Linda Kramer, MSN, RN, CCRN, Louisville, Kentucky Kholoud Khalil, PhD, RN, CCRN, Long Beach, California Stacy Kram, MS, RN, Queen Anne, Maryland Ann Harrington Lalor, BSN, RN, Tacoma, Washington Jennifer Lanter, MSPH, RN, Columbus, Ohio A. Renee Leasure, PhD, RN, CCRN, Oklahoma City, Oklahoma Judith Lindsay, PhD, RN, Round Rock, Texas Robin Lockhart, MSN, RN, Wichita Falls, Texas Alberto Lucchini, RN, Monza, Italy Marta Makielski, MN, RN, CCRN Alumnus, South Bend, Indiana Mary E. Mather, MSN, RN, Castroville, Texas Jen Manganello, MSN, RN, ACNP-BC, Farmington, New Mexico Maximino Martell, Madisonville, Los Angeles Virginia Mason, PhD, RN, Milton, Massachusetts Natalie Susan McAndrew, PhD, MSN, Milwaukee, Wisconsin Colleen McCracken, MSN, RN, CMSRN, CHPN, OCN, Milwaukee, Milton, Wisconsin Vickie A. Miracle, EdD, RN, CCRC, Louisville, Kentucky Angela Wang Nguyen, MSN, AGACNP-BC, CCRN, Philadelphia, Milton, Pennsylvania Sharon C. O'Donoghue, DNP, RN, Boston, Massachusetts Ricardo Padilla, PhD, MSN, RN, San Diego, California Mauro Parozzi, PhD, MSN, RN, Milan, Italy Barbara Phelan, PhD, MSN, RN, Bethany, Connecticut Darlene Petersen, MSN, RN, CCRN, CCNS, Gulfport, Mississippi Kelly Powers, PhD, RN, Charlotte, North Carolina Donna Pineau, PhD, RN, CNE Duxbury, Massachusetts Carmen Rosa Presti, DNP, APRN, ACNP-BC, Coral Gables, Milton, Florida Ellen Redick, MSN, MEd, RN, CNA, CPHQ, Miami, Florida Ruthie Robinson, PhD, RN, FAEN, CNS, CEN, Beaumont, Texas Patricia Reilly, MSN, RN, Centerville, Milton, Massachusetts Lisa Ruth-Sahd, DEd, RN, CEN, CCRN, York, Pennsylvania Erica Sciarra, DNP, RN, APN-C, CCRN, Howell, New Jersey Amanda Shrout, MSN, RN, CCNS, CEN, Lancaster, Pennsylvania Gayle Sturgis, RN, Paul's Valley, Oklahoma Nancy Steffan, PhD, RN, CCRN, CRNP, Lewisville, North Carolina Marion Taylor, MSN, RN, FNP-BC, Los Angeles, California Linda Teplitz, PhD, RN, CCRN, Palos Park, Illinois Carolyn Tennyson DNP, ACNP-BC, AACC, CHSE, Durham, North Carolina Elizabeth Thompson, MBA, BSN, RN, CCRN, Lancaster, Pennsylvania K. Renee Twibell, PhD, RN, Muncie, Indiana Patricia Tuite, MSN, RN, CCRN, Pittsburgh, Pennsylvania Linda Weston Tuttle, MSN, RN, CCRN, Louisville, Kentucky Reba A. Umberger, PhD, RN, CCRN-K, Memphis, Tennessee Mary Lou Warren, DNP, RN, Houston, Texas David Woodruff, PhD, APRN, CEN, CCRN-K, FNAP, Downers Grove, Illinois John J. Whitcomb, PhD, RN, CCRN, FCCM, Clemson, South Carolina Marlot Wigginton, MN, RN, ARNP, CCRN, CCNS, Louisville, Kentucky Paula Wolski, MSN, RN-BC, Boston, Massachusetts Nancy York, PhD, RN, Louisville, Kentucky We continue to support all professionals who wish to learn more about the peer-review process. Wolters Kluwer offers engaging and comprehensive training programs for nurses who would like to become a peer reviewer. The courses are also useful for experienced reviewers who would like to extend and update their skills. The course can be accessed at https://wkauthorservices.editage.com/peer-reviewer-training-course/. This site, sponsored by Wolters Kluwer, offers a basic and advanced course; the basic course is free to all users and includes the following: Three-hour interactive e-learning course (6 modules), including videos and quizzes Discussion forum for Q&A Downloadable peer-review report template Downloadable tools and checklists for different stages of reviewing Methodology and statistics reviewing guide by expert peer reviewers Advanced tips to boost your stature. Practice review assignment with assessment and feedback from course faculty Certificate of completion
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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 | 0.020 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.369 | 0.434 |
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".