Developing a conceptual family needs framework for newcomer immigrants and refugees: A community-based participatory research approach
Bibliographic record
Abstract
Introduction: Family is a major driver of migration. However, the impact of migration on the family as a unit, and family-based service delivery, have not received significant attention in research, service delivery, and policy. The purpose of this project was to explore and develop an avenue to address this gap. Methods: This project used a community-based participatory research approach, encompassing a demographic analysis, a comprehensive literature review, four focus groups, a family survey, and a community seminar, whereby we explored the needs of newcomer families, their use of services, and barriers to resettlement. The demographic data and family survey were analyzed using descriptive statistical analysis. The focus group and community seminar data were analyzed using thematic analysis. Drawing together the findings and cross-cutting themes from the community project, as well as Family Services of Peel’s Framework on Equity, Anti-Oppression, and Anti-Racism; Bierman and colleagues’ gender, migration, and health framework; and Walsh’s family resilience framework, we developed a conceptual Family Needs Framework for service providers working with newcomer families in the Peel Region. Results: This paper discusses the findings from our community-based project. The data presented was collected between May 2022 and April 2023 in the Peel Region in Ontario, Canada. This data is presented as cross-cutting themes from the multiple data sources. We then present the framework developed, Migration and Health: A Systems Approach to Family Support. Our findings highlight that a collaborative, systems approach that view migrants in the context of their family ties and relationships and integrates “basic resettlement needs” and mental healthcare is imperative to holistically understand families’ needs and best support their mental health and well-being. Conclusion: Taking an equity-based, systems-informed, and strengths-based family approach, this framework has the potential to transform how settlement workers support the mental health and related needs of newcomer families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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".