Motivational factors for choosing the degree course in nursing: a focus group study with nursing students.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE WORK: Maintaining the number of new students entering nursing programs and remaining in the nursing occupation largely depends on the ability to recruit and retain young people. The motivational factors that induce young people to choose nursing as a career were investigated through a qualitative research approach. METHODS: Different focus groups were organised involving 32 students at the end of the first year of Nursing. Then the factors affecting their choice of course and the reasons for satisfaction and frustration connected with the course of study were analyzed. RESULTS: The main motivational factors for choosing Nursing that emerged include the following: having done voluntary work in the care area, attraction to the occupation since childhood/adolescence, failure of other plans, possibility to find work, personal acquaintance of nurses. The reasons for satisfaction with the course include: tutor support, workshop activities, placement experience. The reasons for frustration among the students included the complexity and extent of the study plan, elements that often they had not envisaged or had underestimated upon enrolment. CONCLUSIONS: Providing more information on the course of study, the working conditions and characteristics of the nursing occupation, could help young people to make an informed and aware decision, in order to reduce any disappointment and students dropping out of nursing education and attrition in the future. Improving the organisation of the course of study, supporting students' motivation through counselling activities and choosing suitable placement sites, could prevent drop outs.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".