Using Formative Research to Understand Immigrant Settlement in Southern Alberta, Canada
Bibliographic record
Abstract
Background: Worldwide migration is on the rise due to factors such as political turmoil and natural disasters, as well as personal desires for upward mobility and safety. New immigrants face many challenges throughout their settlement into a new community. As Canada welcomes record numbers of new immigrants, it is important that communities across Canada find ways to support new immigrants. The immigrant settlement experience can be improved by identifying key barriers during the settlement process and implementing social marketing approaches to overcome them. Focus of the Article: This article focuses on identifying key barriers to immigrant settlement in Southern Alberta, Canada, using formative research, to provide a foundation for developing social marketing programs with strategic non-profit partners to facilitate immigrant settlement. Research Question: What challenges do immigrants face when settling in Southern Alberta, and how can social marketing efforts facilitate immigrant settlement? Program Design/Importance of the Social Marketing Field: Social marketing can help connect immigrants to resources during their settlement. In this study, we explore how new immigrants access information during their settlement and what barriers they face throughout their settlement experience. We identify ways that organizations can utilize social marketing to better assist newcomers in their settlement, and discuss the importance of taking a participatory research approach. Methods: This research analyzes survey responses from 77 new immigrants in Southern Alberta, Canada. Surveys were conducted in English, Spanish, and Tagalog, primarily online through Qualtrics' survey platform, augmented by eight hard copy responses. Participants were recruited through word of mouth, local non-profit organizations and government offices, and recruiting at community events. Additionally, interviews were conducted with representatives of an umbrella organization from the greater region that supports immigrant settlement and links settlement service providers. Finally, a community-based participatory research group provided additional insights. Results: The leading reason for respondents to move to Southern Alberta was to be with people they know, such as family, spouse, or friends, followed by educational purposes. Broadly, our results suggest that employment, finances, friends and family, and transportation are the primary concerns faced by immigrants. Loneliness can also hinder satisfactory settlement. Survey and interview results suggest that participants had a relatively low level of awareness and usage of nonprofit and civil society organization services during their initial settlement period. Recommendations for Research or Practice: Well-crafted social marketing programs can aid immigrant settlement. Moving forward, the authorship team is further engaging in a community-based participatory research (CBPR) approach to develop a social marketing program to address priority needs in the community as identified by the CBPR team. CBPR helps to assure program design will meet the needs and resources of relevant stakeholders. We call on academic researchers to engage community members when designing social marketing programs. We encourage organizations offering settlement services to utilize social marketing to increase communication efficiencies and improve the settlement experience for new immigrants. Limitations: This research is formative. It is cross-sectional, thus precluding assessments of causality. Although we provide three data sources, we engage a relatively small number of participants in each.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".