PATHS Data Resource: A population-based suite of linkable administrative records and metadata for population health research
Bibliographic record
Abstract
ABSTRACT
 ObjectivesOur objective was to develop a comprehensive longitudinal data resource, which population health research scientists could use to study the social determinants of child health and health equity.
 MethodsThe PATHS Resource was created from data holdings within the Manitoba Population Health Research Data Repository. The Manitoba Health Registry sits at the centre of the Repository and includes information – including a scrambled personal health identification number (PHIN) and date when coverage commenced and expired – on every individual registered with the province’s universal healthcare system. The Repository also includes administrative data spanning several sectors including health, social services, justice, and education. We used individuals’ scrambled PHINs to link children’s administrative records across sectors to build a holistic picture of their health and development. We developed metadata, including routinized SAS algorithms and variable definitions, to ensure consistent operationalization of variables across studies. The longitudinal nature of these data allowed us to construct individual-level health and development trajectories from birth through adolescence for children born from 1984-2014. We used income data from the Canadian Census to develop both indicators of socioeconomic status (average neighbourhood level income) and provincial measures of income inequality (the Gini coefficient).
 ResultsThe PATHS Resource includes data on the social determinants of health as well as health and development for children born 1984 to 2014 (n=608,007). We are able to follow children for a median observation period of 15.4 years. Income inequality – measured using the Gini coefficient – increased from 1984 to 2014: 0.16 to 0.21 (p<0.01). The proportion of children born to women from the bottom income quintile (i.e., the poorest 20% of families) also grew from 23.2% in 1984 to 27.2% in 2014 (p<0.01). When we followed children over their life course, we found that they were most likely to experience poverty (measured by family receipt of income assistance) at 2 years of age (p<0.01). Many studies from a variety of researchers have utilized the PATHS metadata to conduct child health and development research, ensuring consistent variable operationalization. These data have been used to identify policy levers for improving child health and reducing health inequalities.
 ConclusionA resource such as the PATHS Resource can facilitate research into the health and development of children. Having data on the entire population allows investigators to both monitor trends in health inequities and identify strategies for improving health. Metadata ensure variable consistency and comparability across studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".