Bibliographic record
Abstract
PURPOSE: This paper aims to clarify the concept of reflective practice in nursing by using Rodgers' evolutionary method of concept analysis. DATA SOURCES: Literature and references on the concept of reflective practice were obtained from two databases: Scopus and Nursing and Allied health database. Peer-reviewed articles published in English language between 2011-2021 that included the terms 'reflection' and/or 'reflective practice' in the title were selected. Seminal pieces of work were also considered in this analysis. A total of 23 works were included. Most of the selected works addressed the concept of reflective practice in nursing education or nursing practice. DATA SYNTHESIS: The data analysis integrated the stages identified in Rodgers' method of concept analysis to analyze the concept of reflective practice. Analysis of selected works provided an understanding of common surrogates, antecedents, attributes, and consequences of the concept of reflective practice. CONCLUSIONS: Reflective practice is a cognitive skill that demands conscious effort to look at a situation with an awareness of own beliefs, values, and practice enabling nurses to learn from experiences, incorporate that learning in improving patient care outcomes. It also leads to knowledge development in nursing. Considering the current circumstances of the COVID-19 pandemic, this paper identifies the need for nurses to go beyond reflection-on-action and also include reflection-in-action and reflection-for-action as part of their practice. IMPLICATIONS FOR NURSING PRACTICE: This analysis identifies the need for future nursing researchers to develop reflective models or strategies that promote reflection among nurses and nursing students before, during, and after the clinical experiences.
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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 | 0.065 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".