Mapping of early intervention programs for psychosis in France in 2018
Bibliographic record
Abstract
Abstract Early intervention programs (EIP) have been developed in many countries (United States, Europe, Canada) and are now widely considered effective in the treatment of early psychosis. In France, current national policies in the field of mental health promote the development of early intervention but France has not yet met national standards of care for EIP. A recent report from the London School of Economics (2016) even mentioned the delay of France in this area, referring to only one EIP in the country. A preliminary investigation conducted in 2017 showed a strong dynamic with many ongoing projects which led us to renew this investigation in 2018. This was a two-phase study. First, an inventory was achieved through a bottom-up approach and many contacts across the country which may either provide this kind of care or know of such initiatives: psychiatrists, healthcare facilities or Health Regional Agencies. Then, an online declarative survey was administered covering structure of attachment, dedicated team, funding, targeted population, activity in 2017, difficulties and prospects. Between May and October 2018, 69 EIP were identified in France: 35 were operational and 34 were being established or beginners. The 35 identified operational programs were located throughout the country with a few disparities. Half of the programs had been existing for 2 years (17/35). All programs operated with multidisciplinary teams, including at least one psychiatrist and with a mean of 5.9 dedicated full-time equivalents workers. Half of the programs offered case management (48.6%). Most programs were not as a specific setting and had mixed activities, including chronic patients with schizophrenia and most programs offered an integration of ambulatory follow-up in the living environment for some patients (77.1%). A real dynamic has been launched in France. This study will help to improve visibility of programs and to harmonize and ensure a high level of care. Key messages This study shows that a real dynamic has been launched in France. This study shows a need for teams to harmonize and standardize practices of care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".