Time-dependent mood fluctuations in Antarctic personnel : a meta-analytic review
Bibliographic record
Abstract
The third-quarter phenomenon is the dominant theoretical model to explain the psychological impacts of deployment in Antarctica on personnel. It posits that detrimental symptoms to functioning, such as negative mood, increase gradually throughout deployment and peak at the third-quarter point, regardless of overall deployment length. However, there is equivocal support for the model. The current meta-analysis included data from 20 studies (involving 1817 participants) measuring negative mood during deployment to elucidate this discrepancy. Across studies analyses were conducted on three data types; stratified by month utilising repeatedmeasured all time-points meta-analytic techniques, and pre/post deployment data for summer and winter deployment seasons respectively. Moderation analyses were conducted to investigate the impact of personnel's cultural orientation on functioning. Results did not support the proposed parameters of the third-quarter phenomenon, as negative mood did not peak at the third quarter point (August/September) of deployment. Overall effect sizes indicated that negative mood is greater at baseline than the end of deployment for summer and winter deployment seasons, with the direction of this effect influenced by cultural orientation of personnel. These findings have theoretical and practical implications and should be used to guide future research, assisting in the development and modification of preexisting prevention and intervention programs to increase well-being and functioning of personnel during Antarctic deployment.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".