The Influence of Stressors and Strain on Alcohol Use in Canadian Armed Forces Members
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».