Schulung von Medizinischen Fachangestellten bei hausärztlichen Forschungsprojekten. Wissensgewinn und Unterschiede zwischen einer Präsenzschulung und dem Selbststudium eines Schulungshandbuches
Bibliographic record
Abstract
Background When initiating new studies, research institutions are often faced with the question how training can be organized effectively and without wasting resources. We investigated whether training healthcare assistants (HCAs) leads to knowledge gains, and whether differences exist between participation in classroom-based training and self-study of training materials. Methods As part of a research study in family medicine, HCAs either participated in on-site classroom-based training, or were required to study a manual that dealt with the same topics. Six questions were used to assess the level of knowledge (sum score 0–30 points, pts), the results compared using non-parametric tests. Results 73 HCAs participated in classroom-based training. In the knowledge test, their average (avg.) was 19.96 pts before the training and 25.62 pts (p < 0.001) afterwards. Of the 106 HCAs in the self-study group, 27 % (n = 29) did not read the manual (avg. 19.83 pts). Depending on the intensity of self-study, the avg. result among the remaining HCAs ranged from 21.60 to 25.40 pts. HCAs that completed classroom-based training (n = 73) were significantly more knowledgeable than those that studied the manual on their own (n = 77), (p < 0.001). Conclusions A comparison showed that participation in classroom-based training resulted in significantly greater knowledge. A good quarter of participants in the self-study group were not reached at all. To identify further time-effective options, the use of education videos and webinars to train practice teams should be investigated.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".