Understanding context: A concept analysis
Bibliographic record
Abstract
AIMS: To conduct a concept analysis of clinical practice contexts (work environments) in health care. BACKGROUND: Context is increasingly recognized as important to the development, delivery, and understanding of implementation strategies; however, conceptual clarity about what comprises context is lacking. DESIGN: Modified Walker and Avant concept analysis comprised of five steps: (1) concept selection; (2) determination of aims; (3) identification of uses of context; (4) determination of its defining attributes; and (5) definition of its empirical referents. METHODS: A wide range of databases were systematically searched from inception to August 2014. Empirical articles were included if a definition and/or attributes of context were reported. Theoretical articles were included if they reported a model, theory, or framework of context or where context was a component. Double independent screening and data extraction were conducted. Analysis was iterative, involving organizing and reorganizing until a framework of domains, attributes. and features of context emerged. RESULT: We identified 15,972 references, of which 70 satisfied our inclusion criteria. In total, 201 unique features of context were identified, of these 89 were shared (reported in two or more studies). The 89 shared features were grouped into 21 attributes of context which were further categorized into six domains of context. CONCLUSION: This study resulted in a framework of domains, attributes and features of context. These attributes and features, if assessed and used to tailor implementation activities, hold promise for improved research implementation in clinical practice.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".