The effect of an e-learning module on health sciences students’ venipuncture skill development
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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".