Bibliographic record
Abstract
Addiction recovery is a complex, dynamic and non-linear process of change on multiple life domains, and in substance\nuse patterns in particular (Dekkers et al., 2020; White, 2007). While early definitions equated recovery with ‘remission’,\n‘abstinence’ or absence of symptoms of dependence, later conceptualisations have also emphasised the importance of\nhealth, well-being, quality of life, citizenship and social participation, as illustrated in the Betty Ford Consensus Panel\ndefinition (Betty Ford Institute Consensus Panel, 2007, p. 222): ‘a voluntarily maintained lifestyle, characterized by sobriety, personal health, and citizenship’. Although abstinence may be an important feature, it is not a prerequisite for\nrecovery (Laudet & White, 2010). The addiction recovery literature has grown quickly over the last few years, including a\nvariety of recovery indicators, resources and instruments (Ashford et al., 2020; Best & Laudet, 2010; Best et al., 2021;\nCano et al., 2017; Cloud & Granfield, 2008; Groshkova et al., 2013; Neale et al., 2016). Far less is known about how recovery takes place and which mechanisms and mediators are central to addiction recovery.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Editorial framing knowledge gaps in the addiction recovery literature; commentary on a domain's substantive knowledge rather than on research practice.
The work reviews mechanisms and mediators of addiction recovery.
Framing of mechanisms and mediators in addiction recovery; clinical recovery science, not metaresearch methods.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".