Types of analysis of validation studies in nursing: scoping review
Bibliographic record
Abstract
Objective. To identify and map the types of analysis in nursing validation studies. Methods. This is a scoping review with collection carried out in July 2020. The following data extraction indicators were considered: year of publication, country of origin, type of study, level of evidence, scientific references for validation and types of analyses. Data were collected in the following bases: U.S. National Library of Medicine, Cumulative Index to Nursing and Allied Health Literature, SCOPUS, COCHRANE, Web of Science, PSYCHINFO, Latin American and Caribbean Literature in Health Sciences, CAPES Theses and Dissertation Portal, Education Resources Information Center, The National Library of Australia's Trobe, Academic Archive Online, DART-Europe E-Theses Portal, Electronic Theses Online Service, Open Access Scientific Repository of Portugal, National ETD Portal, Theses Canada, Theses and dissertations from Latin America. Results. The sample consisted of 881 studies, with a predominance of articles (841; 95.5%), with a prevalence of publications in 2019 (152;17.2%), of Brazilian origin (377; 42.8%), of the methodological study type (352; 39.9%). Polit and Beck stood out as the methodological reference (207; 23.5%) and Cronbach's Alpha (421; 47.8%) as the statistical test. Regarding the type of analysis, the exploratory factor analysis and the content validation index stood out. Conclusion. The use of at least one method of analysis was evident in more than half of the studies, which implied the need to carry out several statistical tests in order to evaluate the validation of the instrument used and show its reliability
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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 | 0.151 | 0.474 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.079 | 0.083 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".