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Record W3007404774 · doi:10.1016/j.dib.2020.105324

The data on psychological adaptation during polar winter-overs in Sub-Antarctic and Antarctic stations

2020· article· en· W3007404774 on OpenAlexafffund
Michel Nicolas, Guillaume Martinent, Peter Suedfeld, Marvin Gaudino

Bibliographic record

VenueData in Brief · 2020
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of British Columbia
FundersCanadian Space AgencyInstitut Polaire Français Paul Emile VictorConseil régional de Bourgogne-Franche-ComtéCentre National d’Etudes SpatialesEuropean Space Agency
KeywordsExploratory factor analysisAdaptation (eye)PolarConfirmatory factor analysisSample (material)Exploratory researchReliability (semiconductor)Construct validityPsychologyStructural equation modelingConstruct (python library)Applied psychologyClimatologyPsychometricsComputer scienceClinical psychologyStatisticsGeologyMathematicsPhysics

Abstract

fetched live from OpenAlex

The data presented in this article relate to the research article entitled "assessing psychological adaptation during polar winter-overs: The isolated and confined environments questionnaire (ICE-Q)" [1]. These data were acquired in order to develop a standardized instrument - the ICE-Q - designed to assess psychological adaptation within isolated, confined, and extreme environments. A total of 140 winterers from several sub-Antarctic (Amsterdam, Crozet, Kerguelen) and Antarctic (Concordia, Terre Adélie) stations voluntarily participated. Data were collected by multiple self-report questionnaires including a wide variety of well-known and validated questionnaires to record the winterers' responses to polar stations. Data were gathered across two or three winter seasons within each of the 5 polar stations to ensure sufficiently large sample. From four to seven measurement time along a one-year period were proposed to the participants, resulting in 479 momentary assessments. Results of exploratory factor analyses, confirmatory factor analyses, exploratory structural equation modelling, reliability analyses, and test-retest provided strong evidence for the construct validity of the ICE-Q (19-item 4-factor questionnaire). The four factors were social, emotional, occupational and physical. Future studies would examine the dynamic of psychological adaptation in isolated, confined and/or extreme environments during polar missions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.351
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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