Echoes of Home: Mapping Vulnerable Places for Cantonese‐Speaking Immigrants Seeking Family Doctors in the Greater Toronto Area
Bibliographic record
Abstract
Family doctors serve as the initial contact for individuals seeking regular medical service like routine physical exam, diagnosis, and treatment of illness. Nonetheless, immigrant population who do not speak the official language usually prefers receiving healthcare in their own mother tongues. Past studies have focused on exploring accessibility to family doctors speaking Mandarin Chinese, which is not mutually intelligible with another major Chinese language called Cantonese. Despite the significant number of Cantonese‐speaking population in the Greater Toronto Area (GTA) and a recent wave of immigration from Hong Kong, China (hereafter “Hong Kong”) to Canada, little knowledge has been obtained regarding the geographic accessibility to Cantonese‐speaking family doctors. This study seeks to fill the knowledge gap of spatial accessibility to Cantonese‐speaking family doctors in the GTA by using the two‐step floating catchment area (2SFCA) method. By considering the vulnerability in terms of spatial accessibility and attractiveness to the new immigrants from Hong Kong, we have unveiled that more than 90% of neighbourhoods, with below‐median accessibility scores across all five thresholds yet high likelihood of attracting new Hong Kong immigrants, are clustered within four lower‐tier municipalities of Markham, Toronto, Richmond Hill, and Vaughan. This study not only sheds lights on the knowledge gap but also provides timely guidance in formulating public health policies in light of the incoming Cantonese‐speaking immigrants from Hong Kong.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".