Bibliographic record
Abstract
Ensuring employees are competent and confident to perform their duties relies on new employee orientation and ongoing compliance training. Currently, there is no industry standard or evidence-informed decision framework that determines when to use face-to-face, online, or blended learning for healthcare workplace training. This mixed methods research investigated how instructional designers use blended learning to balance the ethical, patient safety, resource, and budget demands inherent in an ever-changing and high-tech workplace, to answer the question: Is there a relationship between delivery modes, interaction type, and perceived risk of the content to be learned in healthcare workplace training? An anonymous online survey asked the opinions of healthcare workplace instructional designers (N = 26) about the use of interaction type and delivery mode for workplace training. The opinions of a subset of participants (n = 19) were analyzed for correlation between their preference for delivery modalities and interaction types in relation to their perceived risk of the content to be learned. Quantitative analysis found: (a) preference for in-person/face-to-face delivery via learner-instructor interaction, specifically for high-risk learning content, (b) less preference for blended learning delivery, and (c) no preference for synchronous online delivery. This study proposes a risk-based instructional design decision-making tool for the healthcare workplace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.506 | 0.315 |
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".