Cultivating social occupations, spaces, and networks: service providers’ perspectives on enabling immigrants’ social participation
Bibliographic record
Abstract
Abstract Introduction Immigrants may experience many barriers to social participation within host societies. Immigrants’ integration into Canadian society is supported through government-funded services. Objective In the present study, we explored the perspectives of service providers working in community organizations regarding their role in enabling immigrants’ social participation through occupations in British Columbia, Canada. Method Representatives from twenty different organizations providing services to the community participated in a constructivist qualitative study aiming to uncover aspects shaping opportunities for occupational participation provided for immigrants. Results Drawing on semi-structured interviews and using thematic analysis, we identified three main themes relating to cultivating social occupations, spaces, and networks. Findings illustrate that service providers’ cultivation of these opportunities can support immigrants’ desired social roles and social identities, further enabling their participation in receiving societies. Conclusion Emphasis upon supporting socio-economic integration into society appears to limit a client-centred approach to developing opportunities for social participation through occupation. Implications for occupational therapists and scientists are discussed.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".