A Model of Psychological Adaptation in Peace Support Operations: An Overview
Bibliographic record
Abstract
Canada has a long and distinguished history of peacekeeping service, yet research from within the Canadian Forces indicates that the psychological and interpersonal toll of these missions on CF personnel can be quite high E.G., 1; 2; 3; 4; 5. The Peace Support Operations Adaptation Model (PSOAM), introduced here, details the adaptation process beginning during predeployment, continuing through the deployment and post-deployment phases. The model adds to existing conceptual models of deployment stress by incorporating individual, group, and organizational level variables at each stage of the deployment cycle, factors assumed integral to short and long term adaptation. Of particular interest are the influence of predeployment factors upon individuals' coping efforts and resiliency. The effects of Personality Factors (e.g., hardiness, self-efficacy, mastery, dispositional optimism, internal locus of control) and predeployment expectations (e.g. deployment goals, beliefs concerning upcoming deployment) on predeployment motivational facto rs (e.g., level of motivation, perceptions of preparedness, perceptions of risk, level of intrapersonal conflict) are of specific concern. These predeployment factors, together with self assessments of coping resources during deployment are assumed to directly affect the quality of adaptation and serve as the primary influences on individuals' resiliency to the stress associated with peace support operations. Our focus on the precursors of the adaptation process also allows us to contribute to efforts to recommend modifications to training content and delivery that may avert later maladaptive responses. Moreover, this focus allows for the specification of individual difference variables of relevance to personnel selection in instances where training cannot completely ameliorate the effects of negative deployment events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".