Motivation and personal challenges while enrolled in higher education: The pathway to becoming a baccalaureate nurse
Bibliographic record
Abstract
Background: The exploration of the non-traditional or self-sufficient financially independent student experiences in higher education are considerably less researched, specifically addressing personal experience while pursuing higher education, when compared to traditional, those who recently graduated from high school, college students. Therefore, the objective of this study was to explore non-licensed hospital employees’ (paraprofessional) perceptions related to their motivation to obtain a baccalaureate degree and overcome personal challenges to become a registered nurse.Methods: A qualitative descriptive focus group research design was used to explore the perceptions of minority paraprofessional employees had related to their motivations and challenges to become a registered nurse. All student- paraprofessionals, who were funded through the Robert Wood Johnson grant program, were invited to participate in this study. A semi-structured interview guide was used to explore the participants’ thoughts about obtaining a college degree and what their motivation is for obtaining a college degree in nursing. Informal questions were used to involve the participants in the discussion. The study was approved by the university’s institutional review board and participants completed an informed consent prior to any data collection.Results: Five participants were engaged in this study. The major theme that these participants reported was the different challenges and sacrifices (finances and life situations) that they had to deal with prior to starting courses and while they were currently taking courses. The second theme was motivation; these participants were motivated to have a better life.Conclusions: These participants had to deal with certain challenges and make sacrifices in order to pursue their dream; however, the participants were able to find the needed motivation to achieve their dreams and become a registered nurse.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".