Off-World Mental Health: Considerations for the Design of Well-being–Supportive Technologies for Deep Space Exploration
Bibliographic record
Abstract
During future long-duration space exploration missions, humans will be exposed to combinations of extreme physical, psychological, and interpersonal demands. These demands create risks for the safety, performance, health, and well-being of both individuals and crew. The communication latency in deep space means that explorers will increasingly have to operate independently and take responsibility for their own self-care and self-management. At present, several research programs are focused on developing and testing digital technologies and countermeasures that support the effective functioning of deep space crews. Although promising, these initiatives have been stimulated mostly by technological opportunity rather than cogent theory. In this perspective, we argue that digital technologies developed for spaceflight should be informed by well-being-supportive design principles and be cognizant of broader conversations around the development and use of digital health applications, especially pertaining to issues of autonomy, privacy, and trust. These issues are important for designing potentially mission-critical health technologies and may be determining factors in the safe and successful completion of future off-world endeavors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".