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Record W7005872965

Separating the soldier from the war: the effects of a physical activity and discussion based program on Canadian military members with combat-related stress

2015· dissertation· en· W7005872965 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2015
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsPhysical activityMilitary personnelStress (linguistics)Value (mathematics)Physical stressOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

Recent global conflicts have increased North American military involvement, leading to higher rates of combat-related stress amongst soldiers. Although physical activity is directly correlated with improvements in mental health, the relationship between physical activity and combat-related stress has not been investigated. Using a mixed methodology approach, in this study, local military and ex-military personnel from Winnipeg, Canada, participated in a six week physical activity program in order to examine the influence of physical activity on combat-related stress. It was found that Canadian military members regularly deal with post-combat stressors, value camaraderie, and consider post-deployment support inadequate. The findings may lead to the integration of physical activity into the healing options of war related emotional stress. Future recommendations from this study are proposed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.004
GPT teacher head0.221
Teacher spread0.216 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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