Cohort Profile: The Montreal Neighbourhood Networks and Healthy Aging (MoNNET-HA) study
Bibliographic record
Abstract
The Montreal Neighbourhood Networks and Healthy Aging study was established: (i) to assess the added value in using formal network methods and instruments to measure social capital and its relationship to health; (ii) to determine whether older adults are more vulnerable to the effects of network and neighbourhood environments; and (iii) to examine longitudinally the relationship between social capital and health among adults in Montreal, Canada. The MoNNET-HA cohort consists of men and women aged 25 years and older, residing in the Montreal Metropolitan Area (MMA). Participants were recruited using a random stratified cluster sampling design with oversampling of adults older than 65 years. Initial MoNNET-HA study participants (n = 2707) were recruited for telephone interviews in the summer of 2008. Since 2008, participants were interviewed in the autumn of 2010 and the winter of 2013/2014. Data currently fall into five categories: (i) social network and social capital; (ii) psychosocial and psychological; (ii) socio-demographic and socioeconomic; (iv) health behaviours and conditions; and (v) neighbourhood environmental characteristics. Healthcare utilization data will be available for a subsample of participants. Upon funding, future work will measure anthropometric and metabolic health directly. Based on agreements with participants, external researchers should request access to data via collaborations with the study group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".