Chasing the Mirage: a grounded theory of the clinical reasoning processes that Registered Nurses use to recognize delirium
Bibliographic record
Abstract
AIM: The aim of this study was to construct a grounded theory that explains the clinical reasoning processes that registered nurses use to recognize delirium in older adults in acute care hospitals. BACKGROUND: Delirium is under-recognized in acute hospital settings, this may stem from underdeveloped clinical reasoning processes. Little is known about registered nurses' (RNs) clinical reasoning processes in complex situations such as delirium recognition. DESIGN: A grounded theory approach was used to analyse interview data about the clinical reasoning processes of RNs in acute hospital settings. METHOD: Seventeen RNs were recruited. Concurrent data collection and comparative analysis and theoretical sampling were conducted in 2013-2014. FINDINGS: The core category to emerge from the data was 'chasing the mirage', which describes RNs' clinical reasoning processes to recognize delirium during their interaction with older adults. CONCLUSION: Understanding the reasoning that contributes to delirium under-recognition provides a strategy by which, this problem can be brought to the forefront of RNs' awareness and intervention. Delirium recognition will contribute to quality care for 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 | 0.047 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".