EXPLORING THE UNDERSTANDING AND APPLICATIONS OF MENTAL SKILLS IN CANADIAN ARMED FORCES FAMILIES: A HOLISTIC APPROACH TO UNDERSTAND THE GAP BETWEEN NEED AND EXECUTION
Bibliographic record
Abstract
Canadian Armed Forces (CAF) families are a population who provide relentless and unwavering support to their associated members and face a multitude of role-specific challenges and experiences. This dissertation aimed to provide a holistic understanding of the types of support existing for CAF families, specifically as they pertain to the use and application of mental skills, and to uncover the needs and desires of these families from various perspectives. Additionally, the purpose of this research was to highlight and address any differences in perspectives between what is currently being sought out by families and what is being actioned by policy makers and stakeholders. The data for this research was collected through four separate studies: a scoping review to understand the current breadth of research; a mixed methods survey and focus group study with CAF families to address their understanding, needs, and desires; an interview study with professionals working with CAF families to gather insight into the present workings and difficulties of the system; and an interview study with individuals working within the areas of resource and program development and implementation to understand their perspective and challenges of providing support. Overall, this dissertation identified key areas of difficulty for CAF families stemming from their experiences with relocations, deployments, and transitions. CAF families are faced with unique challenges because of their roles within the CAF environment and are not always best equipped to handle these challenges. Mental skills were recognized by all participants as a fruitful potential avenue for providing support to CAF families, indicating the importance of these skills for preparatory purposes as opposed to only applying them in response to a specific challenge or issue. The findings of this work identify the need for continued, or initial, evaluation of current support offerings for CAF families and the implementation of evidence-based research for the future design and application of resources and programs within this context. Current programming appears to be catering to some of this population’s specific needs, but there is still work to be done to address all of the challenges of CAF families, all while navigating the complexities of working within an institution governed by many rules and regulations that must be adhered to. This dissertation offers suggestion for policymakers to improve the support of a group claimed to be invaluable within the CAF.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".