Serving Immigrant Families: Using Knowledge Translation to Inform a Family Approach in the Settlement Sector
Bibliographic record
Abstract
Introduction: Immigrant families plan their immigration trajectories and destinations long before they set foot in Canada. Families change and become reconfigured through the immigration and settlement process. It is a life-changing event that results in important sacrifices, changes in gender and family dynamics, living arrangements, and expectations of support. Having the support of a family network is the most important predictor of settlement success (Creese et al., 2008; Lewis-Watts, 2006; Telegdi, 2006). Immigration, Refugees and Citizenship Canada (IRCC hereafter) plays a pivotal role in structuring the newcomer family through immigration policy, and shapes settlement through funding services and programs. IRCC holds the authority to enter into service agreements with organizations and represents the most important source of funding for newcomer settlement services across Canada (except Quebec). Settlement policies and program guidelines determine to whom and how services are delivered. Therefore, they have great influence on newcomer settlement outcomes. On its Settlement Priorities webpage, IRCC (2017a) identifies as a key priority “improving knowledge creation and management through policy-relevant research and knowledge mobilization that […] suggest concrete options for improved settlement service delivery”. Recent academic research as well as findings from the ITIF project demonstrate that immigration and settlement, in a fundamental way, are family experiences. It follows that if the settlement sector is mandated to serve immigrants' needs, they must use a service framework that situates immigrants within these social realities. This paper is designed to inform decision-makers about research findings on newcomer families that confirm the observations of settlement workers and can guide policy. Five key practical recommendations for implementing a Family Approach in the settlement service framework and policies are being presented to IRCC settlement policy-makers. This paper challenges the current individualistic structure of IRCC settlement program design and policy. Ultimately, we seek to improve settlement services for newcomers by influencing multiple levels of the settlement sector to reflect the interconnectedness of newcomer needs with a web of social relations.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".