Place(ment) matters: students’ clinical experiences and their preferences for first employers
Bibliographic record
Abstract
BACKGROUND: Although a significant volume of nursing research has focused on students' experiences of clinical placements, to date, none has considered these experiences in the context of workforce recruitment and specifically how they may impact upon preferences for working for health care providers. METHODS: In this context, the research used a place-sensitive geographical perspective and a combined questionnaire (n = 650), interview (n = 30) and focus group (n = 7) method to collect data on the complex range of clinical experiences which together impact upon the perceived attractiveness of different health care settings. FINDINGS: The data identified a range of experiential factors associated with mentorship, ward management, learning opportunities and racism. An important finding was that although students' experiences are obtained at the micro ward level, even if they may not necessarily reflect what happens throughout the hospital, they potentially impact, both positively and negatively, upon their broader perceptions of the hospital and the likelihood of seeking work there. IMPLICATIONS: The study highlighted a variety of issues that should be addressed by both higher education institutions and hospitals so that they may be able to provide a more consistent and positive experience for students. In the longer term, this may pay dividends through increased recruitment of new graduates.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".