MULTISTAGE SAMPLING FOR TRANSLATIONAL COMMUNITY RESEARCH: ZOOMING OUT TO HONE IN ON PLACE-BASED HEALTH DISPARITIES
Bibliographic record
Abstract
Abstract Whereas researchers strive for generalizability, community-engaged research (CEnR) typically involves only a few specific communities. Drawing on Weberian ideal type, I outline the use of an innovative blended-methods approach to sample the communities in which CEnR practitioners would collect in-depth data. To complement typical practices of entering a community without preconceived ideas, understanding how communities in the sampling frame relate to one another is important for equigenic (place-based health equity) implementations. The selection of neighborhood communities from quadrants in 2x2 matrices allows pertinent concepts to emerge and relevant solutions to be drawn from thriving communities to aid program co-creation and implementation in other communities. For example, this has led to the identification of communities in British Columbia with differing socioeconomic status, social capital, and coping during COVID-19. This methodological innovation is congruent with asset-based community development (ABCD) to minimize arbitrariness in sampling decisions and advance health equity in our cities.
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 | 0.015 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".