Researching Resilience in Bachelor of Science in Nursing (BScN) Students
Bibliographic record
Abstract
Resilience is a significant focus regarding the mental health of public health service workers in Canada.It is also a centre of attention in current nursing research worldwide.Included in this are a broad range of definitions, experiences and approaches to the research itself and to supporting the development of resilience in nurses and nursing students.The authors of this paper embarked on a research study aimed at testing an intervention designed to enhance the resilience and coping skills of students in the BScN program at Vancouver Community College (VCC).It had been observed, anecdotally, by program faculty that students of the program were demonstrating higher rates of stress and less effective coping skills from one cohort to the next.The 'intervention' takes the form of a self-paced, online resiliency program that had previously been tested among frontline responders and found to be effective [1].VCC's Nursing Department formed a research partnership with the authors at the Justice Institute of British Columbia (JIBC) to carry out a similar study.The study is being conducted using a quasiexperimental design which examines students' responses before and after exposure to clinical practice areas.This paper relates the issues that underpin the need for this research, including the significance of studying resilience and coping skills in nursing students.After highlighting the general and specific contexts for the study of resilience, we discuss the importance of the study findings to developing curriculum that can support evidence-informed teaching for resilience and coping skills in the BScN program.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".