The Influence of Stressors and Strain on Alcohol Use in Canadian Armed Forces Members
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".