Stress, Coping, and Psychological Growth in Personnel in a High Arctic Weather Station
Bibliographic record
Abstract
Ideas about positive change following a stressful experience have been of interest to researchers for some time (Antonovsky, 1987; Tedeschi & Calhoun, 1996). A few areas of study have been developed, some with a focus on successful coping such as research on resilience, sense of coherence and hardiness, stress inoculation, posttraumatic growth, and toughening (Tedeschi, Park, and Calhoun, 1998). Suedfeld (2001) has encouraged researchers to focus on the positive effects of work in extreme and unusual environments however, only a few have done so with astronauts, cosmonauts (Ihle et al., 2006: Suedfeld et al., 2012), and high-altitude mountaineers (Smith et al., 2017).To understand the full deployment experience and growth possibilities and to identify the situational impact on the personnel in extreme and unusual environments, a study of stress perception and coping strategies is essential. Work in this domain of stress and coping has been plentiful across many extreme and unusual environments (Leon et al., 2011b; Nicolas et al., 2013; Suedfeld et al., 2009; Suedfeld et al., 2012; Suedfeld, 2015).The current study addresses an additional extreme workplace environment with a combination of stress and outcome variables. Crew members working and living at the Eureka Weather Station (Nunavut, Canada), located on Ellesmere Island at a latitude of 80° North, responded to questionnaires about coping (prior to the mission and postmission), personality (prior to mission), and psychological growth (postmission). Members spent two to four months working in the Canadian High Arctic and faced many environmental and social challenges. Data suggest that the extreme workplace is not as stressful as commonly assumed; further, members use a variety of appropriate coping mechanisms, and postexpedition growth is experienced by all.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".