Assessing nursing clinical skills competence through objective structured clinical examination (OSCE) for open distance learning students in Open University Malaysia
Bibliographic record
Abstract
BACKGROUND: The objective structured clinical skills examination (OSCE) has over the years emerged as a method of evaluating clinical skills in most medical and allied professions. Although its validity and objectivity has evoked so much debate in the literature, little has been written about its application in non-traditional education systems such as in distance learning. This study examined clinical skills competence among practising nursing students who were enrolled in a distance learning programme. The study examined the effect of work and years of nursing practice on nurses' clinical skills competence. METHODOLOGY: This study used observational design whereby nursing students' clinical skills were observed and scored in five OSCE stations. Two instruments were used for the data collection - A self-administered questionnaire on the students' bio-demographic data, and a check list on the clinical skills which the examiners rated on a four point scale. RESULTS: The findings revealed that 14% of the nurses had level four competence, which indicated that they could perform the tasks correctly and complete. However, 12% failed the OSCE, even though they had more than 10 years experience in nursing and post basic qualifications. Inter-rater reliability was 0.92 for the five examiners. Factor analysis indicated that five participant factors accounted for 74.1% of the variations in clinical skills performance. CONCLUSION: An OSCE is a necessary assessment tool that should be continuously applied in nursing education, regardless of the mode of the education program, the student's years of experience or his/her clinical placement. This study validates the need for OSCE in both the design of tertiary nursing degree programs and the assessment of nurses' clinical competency level.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".