Assertive Community Treatment in China – it is time for a made-in-China solution
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
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".