Feasibility of Applying the Life History Calendar in a Population of Chronic Opioid Users to Identify Patterns of Drug Use and Addiction Treatment
Bibliographic record
Abstract
Uncovering patterns of drug use and treatment access is essential to improving treatment for opioid dependence. The life history calendar (LHC) could be a valuable instrument for capturing time-sensitive data on lifetime patterns of drug use and addiction treatment. This study describes the methodology applied when collecting data using the LHC in a sample of individuals with long-term opioid dependence and aims to identify specific factors that impact the feasibility of administering the LHC interview. In this study, the LHC allowed important events such as births, intimate relationships, housing, or incarcerations to become reference points for recalling details surrounding drug use and treatment access. The paper concludes that the administration of the LHC was a resource-intensive process and required special attention to interviewer training and experience with the study population. These factors should be considered and integrated into study plans by researchers using the LHC in addiction research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".