From Enthusiasm to Concern and From Top-Down to Bottom-Up: A Critical Qualitative Analysis of Constructions of the French Model of Opioid Use Disorder Care in the Scientific Literature
Bibliographic record
Abstract
The French Model of opioid use disorder care is frequently cited to advocate for policy responses to the opioid crisis. Prior research reveals a disproportionate emphasis in such citations on federal regulatory changes, raising concerns about overly narrow interpretations and potential missed opportunities for evidence-informed policymaking. We aimed to analyze how the French Model has been used to construct policy responses to the opioid crisis internationally, exploring how unique contexts may shape them. We conducted a qualitative content analysis of scientific references to the French Model, informed by Bacchi's “What is the problem represented to be?” policy analysis approach. We analyzed 120 documents authored by scholars in 21 countries. Two concepts were identified to explain problem–solution constructions within their context: (1) cultural enthusiasm versus cultural concern for pharmaceuticals and (2) top-down, bottom-up, and mixed approaches to change. We mapped the problem solution constructions on a schema developed by intersecting these concepts. The schema had six configurations. Four of the six configurations were represented in the analyzed documents. Solutions were shaped by the various contexts in which they were constructed. They varied from deregulation of opioid agonists as a rapid response in the context of overdose crises to prescription drug monitoring programs as a response to diversion and misuse of buprenorphine. The schema we developed based on two cross-cutting concepts may be used to foster alternative, context-sensitive policy solutions.
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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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.020 | 0.057 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".