MétaCan
Menu
Back to cohort
Record W2190876701 · doi:10.7205/milmed-d-12-00389

Testing a Resilience Model Among Canadian Forces Recruits

2013· article· en· W2190876701 on OpenAlex
Alla Skomorovsky, Sonya Stevens

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMilitary Medicine · 2013
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsSaint Mary's UniversityDepartment of National Defence
Fundersnot available
KeywordsPersonalityCoping (psychology)NeuroticismPsychologyStructural equation modelingHardiness (plants)Psychological resilienceClinical psychologyMilitary personnelBig Five personality traitsApplied psychologySocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Evidence suggests that personal characteristics serve as resilience factors, and may protect military personnel against the development of psychological distress, even during stressful conditions. Structural equation modeling analyses were conducted on data from Canadian Forces candidates undertaking their basic training (N = 200) to test the fit of a model of resilience that is comprised of several individual characteristics, such as personality, hardiness, and coping. The most parsimonious model of resilience with the best fit to the data was identified. This model consisted of neuroticism, military hardiness, and problem-solving coping. The results of the study were consistent with previous research, showing that personality, military hardiness, and coping are important predictors of life satisfaction and health. The proposed resilience model offers a useful approach for the development of training programs to enhance readiness and recovery in the military context.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.001

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 head0.059
GPT teacher head0.363
Teacher spread0.304 · 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