When and How to Evaluate Interrater Reliability of Patient Assessment Tools
Bibliographic record
Abstract
Research Corner| October 01 2011 When and How to Evaluate Interrater Reliability of Patient Assessment Tools Céline Gélinas, RN, PhD Céline Gélinas, RN, PhD Céline Gélinas is Assistant Professor, School of Nursing, McGill University 3506, University St, Wilson Hall, Montreal, QC H3A 2A7, Canada, and Researcher, Centre for Nursing Research and Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada (celine.gelinas@mcgill.ca). Search for other works by this author on: This Site PubMed Google Scholar AACN Adv Crit Care (2011) 22 (4): 412–417. https://doi.org/10.4037/NCI.0b013e31822db44e Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Cite Icon Cite Get Permissions Citation Céline Gélinas; When and How to Evaluate Interrater Reliability of Patient Assessment Tools. AACN Adv Crit Care 1 October 2011; 22 (4): 412–417. doi: https://doi.org/10.4037/NCI.0b013e31822db44e Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentAACN Advanced Critical Care Search Advanced Search This content is only available as a PDF. ©2011 American Association of Critical-Care Nurses2011 Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.311 | 0.664 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.020 |
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".