[Positive deviance: concept analysis using the evolutionary approach of Rodgers].
Bibliographic record
Abstract
Positive deviance is a relatively new concept in healthcare. Since 2006, it has been applied to infection control in order to increase the awareness to good hand hygiene practices. This article focus on presenting analytical results of this concept using the evolutionary approach of Rodgers based on the philosophical postulate that concepts are dynamical and changing with time. For doing so, a census of the writings in nursing, medicine and psychology was carried out. By going through the CINAHL, Medline and PsyclNFO databases using positive deviance as a keyword for the time period: 1975 to May 2012, and in accordance with the method of Rodgers, ninety articles were retained (30 per discipline). The analysis enables one to notice that positive deviance described as an individual characteristic at first, is now used as a behavioral changing approach in nursing and medicine as well. At the end of the analysis and apart from this article, positive deviance will be used in order to study the practice of nurses that adheres to hand hygiene despite limiting constraints within hospital. We will then be able to continue the development of this concept in order to bring it, as Rodgers recommends, beyond the analysis. It would then be an important contribution to good nursing practices in the field of infection control and prevention.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".