A Model for Recruiting Clinical Research Participants With Anxiety Disorders in the Absence of Service Provision
Bibliographic record
Abstract
High-quality research in clinical psychology often depends on recruiting adequate samples of clinical participants with formally diagnosed difficulties. This challenge is readily met within the context of a large treatment center, but many clinical researchers work in academic settings that do not feature a medical school, hospital connections, or an in-house clinic. This article describes the model we developed at the University of Waterloo Centre for Mental Health Research for identifying and recruiting large samples of people from local communities with diagnosable mental health problems who are willing to participate in research but for whom treatment services are not offered. We compare the diagnostic composition, symptom profile, and demographic characteristics of our participants with treatment-seeking samples recruited from large Canadian and American treatment centers. We conclude that the Anxiety Studies Division model represents a viable and valuable method for recruiting clinical participants from the community for psychopathology research.
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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 | 0.111 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".