Tracking the footsteps: a constructivist grounded theory of the clinical reasoning processes that registered nurses use to recognise delirium
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To construct a grounded theory that explains the clinical reasoning processes that registered nurses use to recognise delirium while caring for older adults in acute care settings. BACKGROUND: Delirium is often under-recognised in acute care settings; this may stem from underdeveloped clinical reasoning processes. Little is known about registered nurses' clinical reasoning processes in complex situations such as delirium recognition. DESIGN: Seventeen registered nurses working in acute care settings were interviewed. Concurrent data collection and analysis, constant comparative analysis and theoretical sampling were conducted in 2013-2014. METHODS: A grounded theory approach was used to analyse interview data about the clinical reasoning processes of registered nurse in acute hospital settings. RESULTS: The core category that emerged from data was 'Tracking the footsteps'. This refers to the common clinical reasoning processes that registered nurses in this study used to recognise delirium in older adults in acute care settings. It depicted the process of continuously trying to catch the state of delirium in older adults. CONCLUSIONS: Understanding the clinical reasoning processes that contribute to delirium under-recognition provides a strategy by which this problem can be brought to the forefront of awareness and intervention by registered nurses. RELEVANCE TO CLINICAL PRACTICE: Registered nurses could draw from the various processes identified in this research to develop their clinical reasoning practice to enhance their effective assessment strategies. Delirium recognition by registered nurses will contribute to quality care to older adults.
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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 | 0.059 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".