“It’s All About the Connection”: Digital and Physical Spaces for Mandarin and Punjabi-Speaking Older Immigrants in Calgary
Bibliographic record
Abstract
Social isolation poses significant challenges for older immigrants, particularly those who face cultural and linguistic barriers in their country of settlement. Despite these challenges, little is known about how older immigrants facilitate social connections. This study draws from the Inclusive Communities for Older Adults project to explore how Mandarin and Punjabi-speaking older immigrants utilize physical and digital spaces to mitigate social isolation. Data from semi-structured interviews with 20 older immigrants in Calgary, Canada, was analyzed thematically using deductive and inductive approaches. Findings reveal that physical spaces, such as community centers, facilitate social connections and recreational activities among older immigrants while providing volunteering opportunities that enhance meaningful community engagement. However, challenges such as transportation barriers and harsh winter conditions limit access to these spaces, highlighting the need for more localized and accessible facilities. To overcome these challenges, participants relied on digital platforms to maintain social networks, plan activities, and engage in virtual bonding activities. This study underscores the importance of hybrid approaches integrating community-driven physical and digital spaces to alleviate social isolation in older immigrant populations. Hence, the study recommends culturally and linguistically responsive programming, digital literacy initiatives, and policy measures to improve accessibility and inclusivity of physical and digital spaces.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".