Reflective abilities of nursing students: A thematic analysis of reflection journals
Bibliographic record
Abstract
Objective: Reflective writing is consistently linked to improved clinical decision-making. However, analyzing the journals to evaluate the reflective abilities of nursing students is scanty locally. This study aimed to assess the reflective skills of undergraduate nursing students.Methods: A qualitative thematic content analysis using the Lasater Clinical Judgment Evaluation Rubric was used to assess the reflective abilities of 33 undergraduate nursing students in 138 journal entries. Guided by Gibb's reflective model, the students documented their experiences during a clinical attachment at a National Referral Hospital in Kenya between February and August 2018. Data coding and thematic linking were done using NVIVO version 11. Results: Reflective abilities differed across gender and to some extent across years of study. Most participants were more likely to notice the deviation from the norm, whether patient-related or health care environment-related. Moreover, they demonstrated the ability to respond to the situation, self-evaluate, and develop action plans for future encounters. However, the majority struggled with interpreting findings.Conclusions: Gender differences exist in the way nursing students reflect. Most nursing students focus on describing the situation rather than developing solutions. There is, however, an indication of developing reflective abilities across the year of study.
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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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".