Bibliographic record
Abstract
Many organizations around the world are pursuing space exploration with hopes of going further and further away from Earth. Spaceflight itself has significant implications on humans, meaning that it is important to understand the magnitude of effects that astronauts would feel during these missions. Some pressing concerns are the increased isolation due to social interactions as well as situational factors, which would lead to a decline in mental and physical health. Additionally, the possibility of substance abuse due to stress and access to medications can lead to significant reductions in mental health. To deal with these issues, virtual reality has presented itself as a unique solution that would help provide better overall mental health. The technology is frequently used in various clinical settings to deal with anxiety and depression, through techniques such as exposure therapy and cognitive behavioural therapy. Exposure therapy for anxiety with virtual reality targets anxiety-causing stimulus and works towards changing the patient’s response, in a controlled setting. Cognitive behavioural therapy immerses the patient into a simulated world to provide them with experiences that mitigate the depression they are feeling. On the mission, exposure therapy would potentially be available to deal with stimulants of anxiety, while cognitive behavioural therapy would provide a happiness break. With further research in the field, virtual reality thus presents itself as a feasible opportunity to plan longer duration human space missions. This review compiles and investigates sources from literary research done in the respective fields.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".