Cohort Profile: The Assessing Economic Transitions (ASSET) Study—A Community-Based Mixed-Methods Study of Economic Engagement among Inner-City Residents
Bibliographic record
Abstract
The Assessing Economic Transitions (ASSET) study was established to identify relationships between economic engagement, health and well-being in inner-city populations given that research in this area is currently underdeveloped. This paper describes the objectives, design, and characteristics of the ASSET study cohort, an open prospective cohort which aims to provide data on opportunities for addressing economic engagement in an inner-city drug-using population in Vancouver, Canada. Participants complete interviewer-administered surveys quarterly. A subset of participants complete nested semi-structured qualitative interviews semi-annually. Between April 2019 and May 2022, the study enrolled 257 participants ages 19 years or older (median age: 51; 40% Indigenous, 11.6% non-Indigenous people of colour; 39% cis-gender women, 3.9% transgender, genderqueer, or two-spirit) and 41 qualitative participants. At baseline, all participants reported past daily drug use, with 27% currently using opioids daily, and 20% currently using stimulants daily. In the three months prior to baseline, more participants undertook informal income generation (75%) than formal employment (50%). Employed participants largely had casual jobs (42%) or jobs with part-time/varied hours (35%). Nested qualitative studies will focus on how inner-city populations experience economic engagement. The resulting evidence will inform policy and programmatic initiatives to address socioeconomic drivers of health and well-being.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".