Transitioning from theory into practice in the nursing profession: Challenges experienced by nurses with a CEGEP and bachelor’s degree in a long-term care facility in Montreal
Bibliographic record
Abstract
The purpose of this qualitative study is to examine the different challenges individuals face when entering the nursing profession with either a CEGEP (college) or a bachelor’s degree. I interviewed two groups of newly qualified/registered nurses: one group with a bachelor’s degree the other with a college degree who are currently working in a Long-Term Care Facility (LTCF): seven Registered Nurses (RN), two CEGEP (DEC) and five bachelor (BScN) RNs all of whom agreed that they were not adequately prepared by their education to work in an LTC. Through my research, I identified their individual learning needs based on their educational backgrounds as well as current practice. The research questions that guided the interviews where: \n \n1.\tHow did the entry level competency of CEGEP nursing diploma program prepare you for the workforce in Montreal? \n2.\tHow did the entry level competency of the bachelor’s degree in Nursing prepare you for the workforce in Montreal? \n3.\tDo you feel that your employer supported you in integrating to the clinical setting? \n \nBased on the information received from the participants lived experience I have made suggestions for designing an orientation program for newly hired nurses by LTCFs that takes into account the educational background of the novice nurses to optimize their learning and explore mentoring opportunities or approaches aiming at integrating them into the working environment of an LTCF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".