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Record W2897117779 · doi:10.1017/s0033291718003094

Assertive Community Treatment in China – it is time for a made-in-China solution

2018· letter· en· W2897117779 on OpenAlexaff
Samuel Law, Xingwei Luo, Shuqiao Yao, Xiang Wang

Bibliographic record

VenuePsychological Medicine · 2018
Typeletter
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContent (measure theory)ChinaAssertivenessAction (physics)PsychologyComputer scienceSocial psychologyMathematicsPolitical science

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0060.004
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0270.029
Insufficient payload (model declined to judge)0.0060.002

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 head0.128
GPT teacher head0.466
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2018
Admission routes1
Has abstractyes

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