What Doesn't Kill You Makes You Stronger: Determinants of Stress Resiliency in Rural People of Saskatchewan, Canada
Bibliographic record
Abstract
PURPOSE: This article discusses a research study that explored how rural people in Saskatchewan, Canada, respond to stressful events and adversity, without outside interventions. METHODS: In-depth interviews were conducted with 17 individuals who were living or had lived on a farm in Saskatchewan. The participants' definitions of resiliency, their experiences with resiliency or lack of resiliency, and what they identified as the barriers to and enhancers of resiliency in their lives were discussed. FINDINGS: Resiliency was defined as a process and interactive model that included "bouncing back" from adversity, coping, and acquiring skills, such as problem solving and learning. Resiliency was dynamic, temporal, and relational and was both proactive and reactive. There were both internal and external barriers to and enhancers of resiliency. Barriers to resiliency included fear, isolation, and depopulation, whereas enhancers included resources, support, and control. CONCLUSIONS: Traditional resiliency models are not sufficient for understanding resiliency. It is clear that social, political, and economic factors play an important role in the resiliency and health of people who live in rural areas. A conceptualization of resiliency must be embedded in a social context and include community factors. Recommendations for enhancing resiliency, such as sustaining rural life, supporting families, and providing services, are also discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".