A comparison of skills competency test scores among Philippine-educated nursing students after an intensive medical-surgical course
Bibliographic record
Abstract
Objective : This study examined the effect of a 10-week intensive medical-surgical course on ability to perform 16 common, acute care skills among Philippine educated nursing students seeking licensure in California. The aims of the study were to (1) determine competency in performing skills at the start of the medical-surgical course and (2) evaluate the effectiveness of the medical-surgical course in improving skill competency. Methods : Twenty-three Philippine educated nursing students participated in a 4-hour skills competency test procedure that involved 4 patient care stations and 16 common acute care skills. During the last week of the 10-week medical-surgical course that included 24 open simulation lab practice hours, these same 23 students repeated the testing procedure. Results : At the start of the course skill competency scores were low with many of the participants unable to complete the skills stations. A significant improvement occurred in scores for 14 of the 16 skills when tested in the final week of the medical-surgical nursing course under the same conditions ( p < .05). Conclusions : Conducting the nursing skills competency testing procedure at the start of the course informed faculty about the abilities of Philippine educated nursing student participants. Because of potential differences in nursing education abroad, graduates of nursing programs in the Philippines might benefit from competency testing to evaluate initial skill levels, followed by intensive review of commonly performed nursing skills in the United States, if warranted by initial results.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".