Transitioning from nursing student to clinical teacher in Saudi Arabia
Bibliographic record
Abstract
Despite the remarkable growth in programs and educational facilities in Saudi Arabia (SA) since 1969 when nursing education was introduced, and the influx of government funding to advance nursing education, nursing is often not considered to be a desirable career option or a valued profession in SA. The main socio-cultural reasons contributing to this issue are that nurses traditionally work in mixed-gender environments for long hours and during night shifts, which would cause many female nurses to be away from their families. Thus, newly graduated nurses tend to be employed in roles that are highly respected by society, such as in clinical teaching. However, most novice clinical teachers have not benefitted from front-line nursing experience or formal preparation as educators. Therefore, the purpose of this descriptive qualitative study was to explore Saudi Arabian nursing clinical teachers’ (CTs) (n = 5) experiences of clinical teaching and engaging in student evaluation while employed in a nursing education program in SA. The findings emphasize the struggles experienced by CTs with clinical teaching roles and responsibilities, including student evaluation. Suggestions regarding how teaching roles, responsibilities, and evaluation could be enhanced are also shared. This study was the first study to explore the experiences of nursing clinical teachers in SA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".