MétaCan
Menu
Back to cohort
Record W2794837571 · doi:10.1093/schbul/sby016.309

T33. EARLY INTERVENTION FOR EARLY PSYCHOSIS IN FRANCE, MAPPING OF PROGRAMS

2018· article· en· W2794837571 on OpenAlexaboutno aff
Sophie Meunier‐Cussac, Guy Gozlan, Laurent Lecardeur, Anne Duburcq, L. Courouve

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)Multidisciplinary approachMental healthMetropolitan areaHealth carePolitical scienceEconomic growthMedicinePsychologyBusinessNursingPsychiatry

Abstract

fetched live from OpenAlex

Early intervention programs (EIP) have been developed in many countries (United States, Europe, Canada, Australia) 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) comparing different European countries even mentioned the delay of France in this area, referring to only one EIP in the country. However, different early intervention initiatives have emerged in France during the last decade without such information being centralized and therefore with no visibility on the current situation over the whole territory. The aims of this study were to draw up a comprehensive inventory of existing or planned programs (in metropolitan France and in the overseas territories) in 2017 and to describe how they operate in 2016. This was a two-phase study; phase one was to identify and create an inventory of existing initiatives, and phase two was to describe and conduct an analysis of each initiative. To be included, identified initiatives had to offer an early, intensive and multidisciplinary approach with at least 0.5 dedicated full-time equivalent staff. A secondary inclusion criterion concerned the out-patient setting of the initiative. Inventory was achieved through many contacts across the country, among physicians/psychiatrists, healthcare facilities (hospitals, clinics, adolescent centers…) or administrative institutions (Health Regional Agencies…) which may either provide this kind of care or know of such initiatives. An online declarative survey was administered between March and July 2017 to the identified psychiatrists with questions that covered administrative and clinical topics: structure of attachment, dedicated team, funding, targeted population, activity in 2016, partners of the program, difficulties encountered and prospects. Between March and July 2017, 37 EIP for management of early psychosis were identified in France: 18 were operational, 8 were being established, and discussions were under way for the remaining 11. The 18 identified operational programs were located throughout the country with a few regional disparities. All programs operated with multidisciplinary teams, including at least one psychiatrist and one nurse, and with a mean of 4.3 dedicated full-time equivalents healthcare workers (median: 3.7). Most programs offered case management (12/18), with caseloads ranging from 4:1 to 22:1. The mean caseload was 10:1 (standard deviation 8:1). All programs included 15 to 35 year-old early psychosis patients. Four programs also included patients at ultra-high risk for psychosis (UHR), while 4 others continued patient management during the chronic stages; 4 initiatives included all these stages of the disease. Half of the programs had been existing for 2 to 5 years (50%); 89% were created less than 5 years ago. The surveyed professionals described an increasing number of patients under their care. Numerous projects and discussions appear to be under way (some programs should open very soon). A real dynamic has been launched in France with an increasing focus and this evaluation will help to improve visibility of the identified programs and promote the development of new programs.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.298
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueSchizophrenia BulletinSame topicSchizophrenia research and treatmentFrench-language works237,207