Assertive Community Treatment in China – it is time for a made-in-China solution
Notice bibliographique
Résumé
We appreciate Firn et al.'s comments on our Chinese Assertive Community Treatment (ACT) study.They observed that proving ACT outperforms standard care is akin to proving a Ferrari is superior to a bicycle.This astute observation reflects the current gap between the Western-developed gold standard for community psychiatric treatment, ACT (Dixon, 2000) that we tested, and the standard care available in a low-to middle-income country like China.We are grateful for the chance to engage with this critique.To start, Firn et al. observed rightfully a substantial difference in client contacts between the ACT team and the control.The estimated number of client contacts contrasting the ACT team and the control was roughly 8-10/month v. 0.3-2/month, respectively.This difference, however, must be set in context.From a historical perspective, this differential in service intensity between the study and control arms was akin to the conditions that the original ACT founders Stein and Test (1980) studied in Madison, Wisconsin.Similarly, the validation of the ACT model for the first time in mainland China, where the political, cultural, and socioeconomic conditions are vastly different from other areas that ACT has been studied, makes this RCT study worthwhile.It is particularly notable that the standard care received by the controls was itself part of a major new national program, the Severe Mental Illness Management and Treatment Projectalso known as '686 program'that substantially up-scaled basic community services for millions of Chinese patients (Good and Good, 2012).More generally, studies to identify key ingredients accounting for ACT's success show the sheer number of client contacts alone could not explain its positive outcome (Brugha et al., 2012).Our study has proven that ACT is useable and effective in mainland China, demonstrating that the drivers, road clearance, traffic conditions, and the supporting mechanics are available and suitable for the Ferrari to function in this setting.Firn et al. suggest that flexible ACT (FACT) is a worthwhile alternative.When compared with ACT, FACT serves a wider array of mental disorders, higher number of patients per worker, employing more evidence-based psychotherapies, and has the ability to tailor the intensity of services according to the current level of need of the patient.The preliminary evidence of FACT is very promising (Nugter et al., 2016;Firn et al., 2018) and newer adoptions are expanding (Nakhost et al., 2017).Unfortunately, the resource issues that limit ACT's wide applicability in China at this time -40% of the 18 million people with severe mental illness have never received any treatment (Phillips et al., 2009) are similarly limiting for FACT.FACT uses similar amount of human and financial resources as an ACT (daily meetings, high levels of psychiatrist involvement, a full complement of multi-disciplinary workers), albeit serving 2-3 times more clients (van Veldhuizen, 2007).While potentially a system-changing innovation for developed countries where ACT has been widely adopted, for China, FACT like ACT will still only be a minute part at the top end of the continuum that serves the most severely ill.[One of the authors (SFL) presented and discussed the FACT model in China at the Harvard China Fogerty Conference in 2015 and received a very mixed reception.]In other words, as we peek under the hood, FACT is more like a Lexus and not so much a common Toyota for China.We agree with the call of Firn et al. to reflect on how to develop another 'intermediate model'.It is clear that there is a need for a culturally relevant model that is empirically effective, affordable, and adaptable.One approach is simply to remove some components of ACT and study the impact.Such 'dismantling' studies, to date, are limited and would still be constrained by the ACT original framework (Hu and Jerrell, 1991).In the USA, efforts to understand the 'key ingredients' in ACT [the Critical Components of Assertive Community Treatment Interview (CCACTI)] found highly consensual and internally consistent results from the experts who created ACT in the first place.This original research did become the guiding blueprint for development of ACT henceforth (McGrew and Bond, 1995).The ACT fidelity scales, in their refinements and iterations, were largely based on this foundation (e.g.Monroe-DeVita et al., 2011).Developing a simpler Chinese intermediate model may not find easy guidance there.Other research findings may be more helpful.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,027 | 0,029 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».