Bibliographic record
Abstract
FigureThe mission of the Journal of Pediatric Surgical Nursing (JPSN) is to promote excellence in pediatric surgical nursing practice through educational offerings, nursing research, professional collaboration, and peer support. We, as an organization, are continually striving to reach as many individuals as possible who come in contact with the pediatric surgical patient. By working to reach our international colleagues, we can strengthen our journal and organization as well as further disseminate APSNA members’ important work in pediatric surgery. To assist us in this endeavor, Francis Ian Ross, RN, BAppSc (nursing), MPH (Sydney), FACN (Fellow Australian College of Nursing), has joined JPSN as our International Editorial Board Member. Frank is an APSNA member who many of us have met at our APSNA Annual Scientific Conferences. Frank is currently working as a clinical nurse consultant in the trauma department (Centre for Trauma Care, Prevention, Education and Research) at the Children’s Hospital at Westmead, New South Wales, Australia. His role includes clinical case management of children, quality management of the trauma service, research into traumatic injury in children, and involvement in ongoing injury prevention activities. His research areas have included traumatic death in children, immersions, driveway back-over injuries, pedestrian injuries, post-traumatic stress disorder in staff caring for injured children, and surgical conditions such as Hirschsprung’s disease. Frank has also authored and coauthored multiple articles on pediatric surgical and trauma subjects and has reviewed articles for JPSN. We look forward to his further work with JPSN. In reviewing JPSN’s 2014 data from Lippincott Williams & Wilkins, you can see the international influence JPSN is beginning to have. Ovid, the online subscription database for institutions (universities, healthcare systems, government institutions, and biotechnology companies), has been purchased by 45 different countries. The top six countries with JPSN on Ovid are the United States, Oman, Australia, Turkey, Iran, and Canada. Twenty-five percent of the visits to the JPSN journal Web site are from outside the United States—India, Turkey, United Kingdom, and Canada, making the top countries visiting the journal. Combined stats indicate that an article from JPSN, either on Ovid or the journal Web site, is downloaded every 20 seconds! We also had our first international author, Dr. Esra Tural Buyuk from Turkey, who published in our previous issue (Volume 4, Issue 2) of JPSN! All of this information is very exciting for JPSN, and we look forward to further international manuscript submissions and increasing our international reach via our Editorial Board and JPSN.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.092 | 0.030 |
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".