A salutary welcome: the role of Sense of Coherence and Generalized Resistance Resources in refugee settlement
Bibliographic record
Abstract
Purpose Guided by the salutogenic model of health and well-being, this study aims to use empirical measures of sense of coherence (SOC) and generalized resistance resources (GRRs) to gain a better understanding of the facilitators of successful transition and integration of refugees to Canada and relate these findings to current program development and delivery for the settlement of refugees. Design/methodology/approach Survey research and structural equation modeling. Findings The authors found that newcomers with a stronger SOC were more likely to report successful integration outcomes. GRRs were found to have both direct and indirect effects on the positive settlement of refugees, with the SOC acting as a strong mediator of indirect effects. Research limitations/implications Owing in part, to the disruption caused by the global pandemic, the authors’ data collection period was protracted and the final sample size of 263 is smaller than the authors would have preferred. Another limitation of this study has to do with its cross-sectional design, which limits the articulation of cause-and-effect relationships among the variables. Practical implications In terms of program development and delivery for the settlement of refugees, the authors’ results provide further evidence that refugee participation in socially valued decision-making represents a key determinant of healthy resettlement. Originality/value Much research on refugee settlement originates within “a pathogenic paradigm” that focuses on the stressors and obstacles encountered by people who have been displaced. Taking its cue from Israeli health sociologist, Aaron Antonovsky’s salutogenic model of health and well-being, this study uses empirical measures of Antonovsky’s interrelated concepts of SOC and GRRs to gain a better understanding of the facilitators of successful transition and integration of refugees to a prairie province in Canada and relate these findings to current program development and delivery for the settlement of refugees.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".