A Social Cognitive Career Theory Approach to Understanding the Academic and Career Experience of Practical and Baccalaureate Nursing Students
Bibliographic record
Abstract
Background: There is a current and predicted nursing shortage. Recruitment and retention of nursing students are two of many factors that impact a nursing shortage. Understanding the profile of first semester nursing students and their academic and career development experiences may enhance the recruitment and retention of nursing students, which will help to ease the nursing shortage. In Ontario, the term nursing student includes two subcategories: practical nursing (PN) and Bachelor of Science in Nursing (BScN) students. Methods: Using a case study research design with a mixed methods methodology, the Social Cognitive Career Theory (SCCT) interest, choice, and performance models guided the exploration of first semester PN and BScN students. Sixty-nine students (32 PN; 37 BScN) completed a comprehensive survey questionnaire, 14 of whom also participated in interviews. The two programs were situated in the same geographical location. Factor analysis and multiple regression were used to assess the ability of the SCCT model variables (past performance, self-efficacy, outcome expectations and goals) to predict student performance as measured by first semester grades. Results: The main part of the study identified the similarities between PN and BScN students and highlighted the differences which were not captured with the use of the inclusive term nursing student. The main differences identified among the two groups included career choice factors, academic confidence, and career confidence. In addition, the SCCT model variables explained 48% of the variance for PN students and 40% of the variance for BScN students. Of all the independent variables, anatomy and physiology confidence and career confidence were statistically significant for PN students and for BScN students it was only anatomy and physiology confidence. Conclusions: Identifying the similarities and differences of PN and BScN student career development is important information for effective career counseling, recruiting, marketing, curriculum development, teaching, and policy development as they relate to the retention of nursing students, especially in times of nursing shortages. This study reinforces the importance of assessing not only the students’ academic performance as they move through the first semester of a nursing program, but also the students’ career confidence.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".