Developing capacity to care for clients at risk for delirium and for acutely delirious clients
Bibliographic record
Abstract
The purpose of this mixed-methods study was to assess nurses’ (N = 56) knowledge and self-efficacy of: a) caring for clients experiencing delirium, and b) caring for clients at risk of developing delirium in a hospital setting. Nurses completed study instruments prior to and immediately after taking part in a clinical education session. Seven nurses participated in a follow-up interview to explore their experiences of applying knowledge from the education session to the practice setting. Objectives of the education session included reviewing risks, signs, symptoms, standardized screening tools, and management strategies for clients at risk for or experiencing delirium in a hospital setting. Nurses were found to have improved knowledge and self-efficacy, as to the recognition and management of delirium. The qualitative findings highlighted nurses’ rich experiences and revealed themes, namely, enhancing emotional intelligence, strengthening clinical judgment to enhance quality of care, and increasing competency for family care. This study demonstrates how continuing education in clinical practice can positively impact nursing knowledge, confidence, and application of knowledge into practice in efforts to decrease the prevalence of delirium. As such, an investment in continuing professional development education for delirium recognition and management is proposed to be a strategy that can positively impact client care.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".