Concept analysis: the importance of differentiating the ontological focus
Bibliographic record
Abstract
AIM: The aim of this paper is to clarify the philosophical underpinnings of concepts and concept analysis and the implications of their use through the lens of particular ontological perspectives. BACKGROUND: Information on the philosophical foundations of concepts from an ontological and epistemological perspective is not readily identifiable in the international literature. Although some authors have made reference to the ontological perspectives of specific concept analysis processes, none have addressed the implications of the realist or relativist perspective in relation either to the analysis process or the implications of a particular ontological perspective on the meaning and utility of a specific concept. METHOD: We describe the evolution of concept analysis and influence of ontological paradigms on specific analysis methods. Using an historical review of concept development within nursing thought, we decode the language of concepts and processes of concept analysis, outline the importance of the ontological foundation of concept development, and describe the impact of concept use. DISCUSSION: The nursing literature is dominated by concepts created from a realist perspective. Although recent nurse-authors have introduced evidence-based data to facilitate the development of a number of concepts, they have held fast to the perception that the 'best', most adequate or mature concepts transcend context. CONCLUSION: The theoretical shift from context-bound empirical analysis of concepts belies the complexity of nurses' work. Concepts are unapologetically context-bound. A concept that transcends context (based on realist ontology) will remain the same even when the context of praxis changes limiting its utility.
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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 | 0.096 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| 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".