Education and employment training supports for newcomers to Canada’s middle-sized urban/rural regions: Implications for social work practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The last decade has witnessed the movement of immigrants from Canada’s largest urban centers—Toronto, Vancouver, and Montreal—to smaller urban-rural communities. Nevertheless, very little scholarship exists on newcomer integration in these communities. Furthermore, social work literature examining the perspective of service providers who work with newcomers is lacking. Grand Erie is a middle-sized urban/rural region in Ontario, Canada that is experiencing increased migration of newcomers. This paper focuses on a part of a larger Community-based participatory research on ‘Newcomer Settlement and Integration in Education, Training, Employment, Health and Social Support’ in Grand Erie and discusses the findings in the education and training domain. Data were gathered from 212 newcomers (men and women) and 237 service providers using survey questionnaires. Findings Most of the newcomers in this study had not taken any education or employment courses post-migration. The qualitative and quantitative responses from participants (newcomers and service providers) highlight a lack of affordable child care and poor transportation infrastructure in this region as significant barriers to newcomers’ ability to take education or employment courses especially in case of visible minority women. Applications The results of the study suggest that there is an opportunity for social workers to build partnerships with community agencies as well as with policy-makers at regional and provincial levels to foster the social, economic, and political integration of new immigrants in the host society.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it