Assessing and monitoring clinical practice of undergraduate nursing students: a middle eastern context
Bibliographic record
Abstract
This paper presents an innovative Weekly Clinical Skills Progress (WCSP) tool to support the assessment of undergraduate nursing students in their clinical placements. The WCSP tool was implemented at the University of Calgary in Qatar (UCQ) Nursing Program in Spring 2024 to address inconsistencies in assessment documentation related to the absence of clearly defined proficiency levels in clinical courses. The UCQ clinical faculty trialed the newly developed WCSP tool on eighty-seven third-year nursing students enrolled in the clinical course Nursing Practice for High Acuity and Chronic Conditions. These students were divided into 11 groups, each consisting of six to seven members per instructor, and were placed in various medical-surgical clinical sites throughout Hamad Medical Corporation (HMC) in Qatar. During the course implementation and following, feedback from faculty, students and buddy nurses indicated the WCSP tool clarified the clinical goals, enabled consensus on clinical proficiency levels according to the course outline, and assessments were more consistent. Though the WCSP tool is still being refined, and more qualitative and quantitative research is needed, this paper contributes valuable preliminary results and recommendations that benefit nursing programs worldwide.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".