How to Prepare a Nursing Informatics Conference Submission
Bibliographic record
Abstract
This workshop aims to demystify the process of submitting papers to Nursing Informatics and MEDINFO Congresses for international authors. During the workshop the authors of this proposal will focus on the characteristics of NI and MEDINFO congresses, principles of scientific writing, requirements of submission formats, and criteria used for assessing submissions. The workshop will be of special interest to those who are planning on submitting a paper, poster, or workshop to future Nursing Informatics and MEDINFO conferences. As part of this the workshop, authors and participants will discuss and share their experiences in submitting papers to NI and MEDINFO conferences and the workshop authors will provide suggestions on how to improve the papers.
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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 | 0.160 | 0.350 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.029 | 0.019 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.093 | 0.103 |
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".