Nursing concept analysis in North America: state of the art
Bibliographic record
Abstract
The strength of a discipline is reflected in the development of a set of concepts relevant to its practice domain. As an evolving professional discipline, nursing requires further development in this respect. Over the past two decades in North America there have emerged three different approaches to concept analysis in nursing scholarship: Wilsonian-derived, evolutionary, and pragmatic utility. The present paper compares and contrasts these three methods of concept in terms of purpose, procedures, philosophical underpinnings, limitations, guidance for researchers, and ability to contribute to nursing knowledge and disciplinary advancement. This work extends prior criticisms of concept analysis methods, especially as formulated by Morse and colleagues, by promoting further critical discussion regarding the direction and effectiveness of nursing efforts to meet the basic needs of disciplinary development. Its central thesis is that nursing concept analysis must advance beyond the Wilsonian-derived methods of Walker and Avant by devoting greater attention to understanding the domain of concepts to be analysed and deriving features from these contexts.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".