Linking the Human Flourishing Study to the Existing Health Datasets for Immigrants in Ontario, Canada
Bibliographic record
Abstract
Relatively little is known about how country-level measures of human flourishing affect the health outcomes of immigrants in their host countries. This study presents an in-depth, cross-national examination of the variations in health outcomes among immigrant groups. We will utilize data from the Human Flourishing Study. Led by Byron Johnson of Baylor University, and Tyler VanderWeele of Harvard University, the HFS is a five-year longitudinal study aimed that aims to better explain human flourishing. The HFS data consists of an internationally diverse sample of over 207,000 individuals from 22 countries. We will use this data to determine whether flourishing index values, continuously ranging from 0 to 10, are associated with the health outcomes of immigrants to Canada. We hypothesize that as country-based flourishing increases among immigrants, the associated risk of premature mortality will decrease. In order to accomplish this, we will import the HFS data from all 22 countries into the Ontario ICES data repository which contains the administrative health and demographic records of nearly 13 million people. The HFS data are country-specific, enabling the creation of ecological-level attributes for each of the 22 HFS countries, and hence, the respective immigrant groups to Ontario from each of those 22 countries. Using multivariable modified Poisson and Cox proportional hazard models, we will assess associations between country-specific flourishing and health outcomes such as self-harm, and intentional injury, as well as pregnancy-related outcomes like preterm birth, and perinatal morbidity. Such novel information is important, as the HFS measures extend far beyond the classic measures of mental health and physical disease. Given that Ontario receives 46% of all immigrants to Canada, it is an ideal place to complete this project.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".