Revisiting Pseudo-Haptics for Psychomotor Skills Development in Online Teaching
Bibliographic record
Abstract
In a centralized model of simulation-based education (Ce-SBE), the trainees practice clinical skills in simulated laboratories based on physical models, while in a decentralized model (De-SBE), the trainees practice these skills outside of these laboratories. Attention to De-SBE has drastically shifted to virtual learning environments (VLEs), serious games, and virtual simulations employing various digital technologies, including virtual, augmented, and mixed reality. In particular, remote learning has grown immensely during the COVID-19 pandemic as traditional in-person teaching and training activities are conducted online as a form of facilitating continuity in education. VLEs allow trainees to learn from virtual simulated health experiences in an interactive, engaging, and ethically safe manner, while providing educators the opportunity to implement simulated experiences to a larger number of learners. Despite these benefits, for certain types of clinical skills, such as psychomotor skills, VLEs have not yet reached their potential. This is primarily due to technical limitations and cost issues with the haptic devices required to simulate the sense of touch. Pseudo-haptic refers to the illusion of haptic stimulation in the absence of mechanical haptic interfaces and often combines the use of a passive input device (e.g., mouse) with visual and auditory feedback to simulate haptic properties (stiffness or friction of an object). Although the application of pseudo-haptics for psychomotor skills development is still in its infancy and currently trending due to the availability of consumer-level technologies, the potential to present haptic cues in the absence of active haptic devices may allow trainees to practice some tasks outside of research and training labs. The implications of pseudo-haptics are tremendous, particularly as remote learning becomes more widespread, and warrant further discussion.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".