PERSTEMPO in the Canadian Forces: The Role of Coping and Cohesion in the Relationship between Job Stress and Morale
Bibliographic record
Abstract
Following a decade of increased frequency and intensity of international and domestic operational deployments by the Canadian Forces (CF), a range of anecdotal data gathered in 2000 suggested that significant personal difficulties were being experienced by military members. The PERSTEMPO (Personnel Tempo) and Human Dimensions of Deployments Study (HDDS) was established in order to meet the growing awareness of the need to assess the human dimensions of performance among military personnel. In addition to deployments, day-to-day aspects of job functioning may have a significant effect on the morale of military members. It is important to recognize the factors that may influence the relationship between job-related stress and morale, so that efforts and resources to increase the operational effectiveness of the CF can be directed appropriately. To this end, the present paper explored the buffering effects of horizontal cohesion, vertical cohesion, and coping on the relation between job-related stressors and morale in the CF. It was found that vertical and horizontal cohesion moderated the effects of job stressors on unit morale, such that the negative effect of job-related stressors on morale in units was attenuated if members experienced high levels of cohesion in their unit and cohesion with their superiors. Moreover, job stressors and cohesion had direct effects on personal morale. Coping strategies did not act as moderators, but instead had strong additive effects on morale, such that high levels of active coping, and low levels of passive coping, predicted higher morale. The findings are discussed in terms of the implications for programs and resources aimed at increasing morale among military members and units.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".