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Record W2950553475 · doi:10.22215/etd/2019-13481

The Influence of Stressors and Strain on Alcohol Use in Canadian Armed Forces Members

2019· dissertation· en· W2950553475 on OpenAlexaffabout
Karen Rankin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsStressorContext (archaeology)Military serviceConsumption (sociology)Depression (economics)PsychologyAlcoholEnvironmental healthMedicinePsychiatryPolitical scienceHistoryChemistry

Abstract

fetched live from OpenAlex

Historically, alcohol consumption in the military has been widespread, especially postdeployment, which causes concern for the leadership of the Canadian Armed Forces (CAF) in the post-Afghanistan deployment era.In order to shed light onto this important issue, two studies assessed the impact of multiple stressors and strain on alcohol consumption utilizing a stressor-strain-alcohol consumption model (SSAC model).Moreover, generational differences in alcohol consumption as well as various elements of the SSAC model were examined.In Study 1, an initial model identified the influence of pre-military service life stressors on alcohol consumption in recently enrolled members and found that increases in Negative Life Events and Exposure to Violence in their preservice lives were associated with increases in alcohol consumption, and that these associations were mediated by posttraumatic stress disorder symptoms (PTSD).Interestingly, Millennials were found to consume more alcohol than both Gen Xers and Late Baby Boomers, and they also demonstrated weaker associations between Childhood Neglect/Depression, Childhood Neglect/PTSD, and alcohol consumption/Negative Life Events than did Gen Xers.Equally noteworthy, Gen Xers and Late Baby Boomers consumed alcohol to the same degree.In Study 2, a revised model was tested in the postdeployment context with Combat Exposure as the stressor.Also, baseline information from pre-enrollment (Time 1) was controlled in the post-deployment SSAC model (Time 2) to further elucidate the impact of Combat Exposure on strain and alcohol consumption.Results indicated which stressors, namely Negative Life Events and Childhood Adversity, and strain (i.e., Depression and PTSD), had cumulative, long-term effects on members' alcohol consumption.Generations did not differ significantly on alcohol consumption, but it was noted that Millennials demonstrated weaker association between Time 1 and Time Dr. Jennifer Lee challenged me and greatly contributed to my understanding of the effects of alcohol consumption in stress-strain and post-deployment contexts; my ability to conduct, interpret, and write statistical analysis, especially mediation; and pushed my creative research boundaries.I will not be able to repay her for her selfless efforts and massive amounts of time and patience.Dr. Janet Mantler provided sound advice on the thesis process, insightful feedback on my thesis, and guided me to my thesis topic, all of which were invaluable.Second, to my many friends who have been an integral part of me achieving this goal, whether that be through academic support or distraction through friendship and fun (or both), I could never thank all of you adequately.Specifically, I would like to thank my friend Joy Klammer for her counsel, sanity checks, encouragement, especially when times were tough, and research and grammatical acumen that were all blessings.Without a doubt, Kevin Rounding was a great sounding board for statistical topics and he provided me with statistical guidance and, often, education.I would also like to thank my friends who understood, supported, and encouraged me to finish this thesis, including but not limited to Michelaine Lahaie, Kathleen Currie, Krista Leonard, Aoife Brennan, Elisa Cass, and, from afar

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)0.003
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 score0.021
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.058
GPT teacher head0.393
Teacher spread0.334 · 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
Published2019
Admission routes2
Has abstractyes

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