Pushing through: A grounded theory of undergraduate nursing students making meaning of clinical-related critical incidents
Bibliographic record
Abstract
BACKGROUND: Undergraduate nursing students are at risk of exposure to clinical related critical incidents. The impact on nursing students and the use of coping mechanisms and processes to make meaning of these distressing experiences are poorly understood. PURPOSE: The aim of this study was to provide a theoretical understanding of the processes that undergraduate nursing students use to make meaning of clinical related critical incidents. METHOD: Following the constructivist grounded theory method, a purposive sample was used to recruit 15 students enrolled in a Canadian university baccalaureate nursing program. Data were collected using semi-structured interviews over 12 months. Interviews were transcribed and analyzed using constant comparison and concurrent data collection and coding. RESULTS: Three theoretical categories emerged from the data to explain the processes used by nursing students to make meaning of their critical incidents: distress, identity shift, and moving forward. The processes described were characterized by an overarching category of "Pushing Through." CONCLUSION: The resulting theory of "Pushing Through" can be used as a lens for students and nurse educators to navigate making meaning of clinical related critical incidents. This theory highlights the significance of students' self-understanding as they explore and assemble individualized strategies and coping skills. Educators who teach in the clinical setting should prioritize the development of a trauma-informed learning environment by establishing connections and transparent expectations with learners.
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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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".