An Alternative Framework to Investigating and Understanding Intraindividual Processes in Substance Abuse Recovery
Bibliographic record
Abstract
BACKGROUND: Sustained recovery from substance abuse is a dynamic intraindividual-level process. OBJECTIVES: We argue that research on recovery process will benefit from a theoretical approach that captures both the dynamic and the idiographic nature of substance abuse recovery. In addition to setting out why we believe that research on recovery can benefit from such an approach, we provide a demonstration of idiographic within-individual analyses of between- and within-day associations among negative affect, substance use craving, and positive social experiences. DESIGN AND SUBJECTS: The data used were drawn from 39 abstinent young adults in 12-step recovery from substance abuse (mean age = 22.9, females = 12). Participants provided an average of 26.7 days of daily diary data by end-of-day collections. Unified first-order structural equation models were fit individually to predict daily levels of craving and negative affect from the previous day's same two variables as well as from both the previous day's and the same day's positive social experiences. RESULTS: Model estimates demonstrated substantial interindividual heterogeneity in their day-to-day associations in both direction and magnitude, highlighting the importance of applying idiographic approach to understanding recovery. Cluster analyses were subsequently applied to individual model estimates to identify homogeneous subgroups that demonstrated similar day-to-day association patterns, revealing two distinct subgroups that appeared to manage daily abstinence through different mechanisms. CONCLUSIONS: The idiographic approach presented provides the potential value of framing recovery as an idiosyncratic dynamic process and provides targets for tailored and adaptive treatment and recovery supporting intervention in future design and evaluation.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".