Exploring the relationship between sense of belonging and perceived well-being in Canadian Army reservists
Bibliographic record
Abstract
Introduction: Army reservists are embedded in two worlds, one military and another civilian, making them “dual citizens” of two distinct but interconnected communities. This research sought to determine how a sense of belonging to a reserve unit, the Canadian Armed Forces (CAF), and the local community relates to army reservists’ overall well-being. Close attention was paid to the role of social support and resilience as possible mediators in this relationship. Methods: A total of 1,154 CAF army reservists completed a survey assessing their sense of belonging, resilience, social support, and well-being. Path analyses were computed to determine the relationship between sense of belonging (in the community, reserve unit, or CAF) and overall well-being, with resilience and social support as mediators in this relationship. The sample was randomly split in half to determine whether the results of the path analyses could be replicated across two samples. Results: The results of path analyses indicated that reservists who felt a greater sense of belonging to their communities reported higher levels of overall well-being (i.e., direct effect). This relationship was partially due to their higher levels of social support and resilience (i.e., indirect effects). Similar results were not obtained in the case of belonging to one’s reserve unit and the CAF. Findings are explained through the lens of the centrality, or lack of centrality, of reserve identity. Discussion: Findings suggest a sense of belonging to the community plays a positive role in enhancing the well-being of Canadian Army reservists.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".