Quilting Stories and Embracing Culture: An Arts-Informed Narrative Inquiry Exploring the Experiences of an Older Chinese Canadian Immigrant with Depression
Bibliographic record
Abstract
Chinese immigrants tend to underutilize mental health services. Cultural and\nlinguistic barriers may discourage Chinese immigrants from accessing these services.\nYet, a paucity of qualitative research that explores the experiences of older Chinese\nCanadian immigrants with depression exists in the mental health literature. This study\nexplored how older Chinese immigrants (age 55+) experience depression, and what their stories reveal about the sources of mental health support that they use. Using arts-informed\nnarrative inquiry methods, I conducted a series of five research sessions with a\nco-participant from the Chinese Canadian community in the Greater Toronto Area.\nNarrative patterns regarding identity, voice, and communication, as well as a prominent\nnarrative thread of relationship, emerged from my co-participant???s story. This study\nillustrates the heterogeneity that exists within this group, and illuminates the value of a\nperson-centered and culturally safe approach to providing mental healthcare to older\nChinese immigrants with depression.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".