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Record W2067061041 · doi:10.7205/milmed-d-10-00325

Role of Hardiness in the Psychological Well-being of Canadian Forces Officer Candidates

2011· article· en· W2067061041 on OpenAlexafffundabout
Alla Skomorovsky, Kerry Sudom

Bibliographic record

VenueMilitary Medicine · 2011
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDepartment of National Defence
FundersMinistère de la Défense Nationale
KeywordsHardiness (plants)OfficerMilitary personnelPsychologyConstruct (python library)Social psychologyClinical psychologyApplied psychologyPolitical science

Abstract

fetched live from OpenAlex

Previous research has found that hardiness is associated with greater psychological well-being and lower levels of stress. This study examined the role of hardiness in the psychological well-being of military officer candidates undergoing basic training. Although most researchers have conceptualized hardiness as a global psychological construct, it is possible that military-specific hardiness, which pertains specifically to work experiences in the military environment, may be a more relevant measure. The role of both general and military-specific hardiness in life satisfaction, health symptoms, training satisfaction, and training stress was examined. The results of this study were consistent with those of previous research, suggesting that military-specific hardiness is an important predictor of psychological well-being of military personnel. Furthermore, military-specific hardiness served as a better predictor of the psychological well-being of military personnel than general hardiness. The implications of the findings and future research suggestions are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.677
Threshold uncertainty score0.643

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.347
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2011
Admission routes3
Has abstractyes

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