The effect of an e-learning module on health sciences students’ venipuncture skill development
Why this work is in the frame
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Bibliographic record
Abstract
Venipuncture is a psychomotor skill required in many healthcare professions. E-learning could be used to overcome current barriers in face-to-face learning in healthcare education such as insufficient classroom space or qualified instructors. We sought to evaluate the effectiveness of an e-learning module on students' performance when used in addition to in-class training. Methods: Overall, 224 health sciences students were approached to participate in this pilot study. Recruited students were divided into control and study groups. The control group received only in-class training, whereas the study group had access to the e-learning module in addition to in-class learning. Both groups were evaluated on their self-confidence using a Likert scale, academic competence using a multiple-choice questionnaire, and psychomotor competence from video skill recordings using an in-house rubric. Nonparametric, independent sample Mann-Whitney tests were performed to evaluate differences between groups. Results: Overall, 114 students provided written informed consent; 84 students (control: n = 50, study: n = 34) participated in at least one component of the study. Significantly higher (p = 0.017) academic competence scores were observed in the study group. Significantly higher confidence levels were also observed postintervention for both the control (p = 0.0025) and study (p = 0.0011) groups; however, no significant differences were found between the study and control groups before (p = 0.441) or after (p = 0.883) intervention. Finally, no significant differences (p = 0.428) were observed for psychomotor skills between the study arms. Conclusion: Our results suggest that there is potential for e-learning to increase the academic competence of students when used in conjunction with traditional learning; however, further research is needed to determine its efficacy on psychomotor skills.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it