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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 it