An exploratory mixed methods study of urban and rural registered nurses’ experience of clinical reasoning
Bibliographic record
Abstract
Background and objective: Engaging in clinical reasoning frequently occurs in busy, high pressured, stressful settings with competing demands. Patient outcomes are affected in part by RNs’ clinical reasoning ability. This study aims to explore the extent to which the clinical context influences clinical reasoning among urban and rural registered nurses.Methods: In this exploratory study using a mixed method approach, 11 rural hospital RNs and 7 RNs practicing in urban medical or surgical units completed a survey and a semi-structured individual qualitative interview. Data were generated over a two month period in 2015. Descriptive statistics and Mann-Whitney U was used to test for differences among groups. Qualitative data analysis procedures were used to help identify two major themes.Results: The perceived lack of time influenced the participants’ ability to engage in clinical reasoning. The findings also suggest that rule following hampered the participants’ ability to confidently share their clinical reasoning.Conclusions: To deepen RNs clinical reasoning an examination of the clinical environment’s structure and processes that support or impede engagement in clinical reasoning is required. Specific strategies that enhance clinical reasoning need to be unit specific and driven by RNs.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".