Bibliographic record
Abstract
Medical procedures, like IV insertions and pin removals, may cause pain and anxiety in children. While preventable, high rates of procedural pain persist in hospitals. The use of distraction, such as virtual reality (VR), offers a non-pharmacological approach for pain and anxiety management during medical procedures. More specifically, VR is an immersive technology that brings the user into a three-dimensional world that looks and feels real. The illustration depicts how VR works to decrease pain perception through the analogy of a tug-of-war between pain signaling and VR. During a medical procedure, a child``'s attention may be focused on the IV poke, increasing pain perception. However, if the child is immersed in a VR game during their medical procedure, the VR pulls the brain's attention away from the IV poke towards an imaginary and pleasant world. As VR is immersive and interactive, it consumes more attention than pain, thereby decreasing pain perception, and winning the tugr-of-war. Despite the evidence for VR, there is a 20-year gap in the implementation of VR across child healthcare settings. Our team is currently investigating the barriers, facilitators, and contextual challenges for VR use in child healthcare, and in parallel developing tools to disseminate research evidence and facilitate intergation of VR into the standard of care. This illustration serves as reminder of how VR is thought to help with pain management. To learn more about VR, visit: https://www.mcgill.ca/virtualrealityforchildcare/
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.448 | 0.162 |
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".