Factors linked with the modification of mental health score of Peruvian personnel in Machu Picchu Antarctica base
Bibliographic record
Abstract
Introduction Isolation of people needs significant support, potentially impacting their mental well-being for future assignments. Objective This study aimed to examine whether the military institution or other factors were associated with changes in the mental health of Peruvian navy and army personnel in Antarctica. Methods The exploratory study employed a prospective cohort design, conducting surveys at two different points during the trip. Various factors such as stress, anxiety, depression (measured using DASS-21), and post-traumatic stress (measured using SPRINT-E) were assessed, alongside other variables. Results The outcomes indicated noteworthy changes in the participants' scores. Specifically, stress scores increased among navy personnel but decreased in the army. Moreover, anxiety scores decreased among those with previous trips and increased among those without. Additionally, anxiety scores remained stable in the navy but decreased in the army. Furthermore, depression scores increased in the navy and decreased in the army. The study also found that older age was associated with higher post-traumatic stress scores. Additionally, post-traumatic stress scores decreased among those with technical studies and increased among those with university studies. Conclusion The exploratory findings suggest that the institution to which the personnel belonged and other characteristics of the delegations may be linked to changes in the scores of the evaluated mental health factors. These findings could offer valuable insights for future delegations facing similar conditions. Future studies should increase the sample size to allow for a more detailed analysis of the effects and to enhance the generalizability of the findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".