What is known about flexible assertive community treatment across populations and contexts? A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The objective of this scoping review is to elucidate contexts in which Flexible Assertive Community Treatment (FACT) has been utilised, which populations it has served, how it has been adapted and what outcomes it has achieved. FACT is a model of mental healthcare where patients are transitioned along a continuum of high-intensity outreach-based treatment and lower-intensity case management, according to need. Despite being adopted globally, a review of the evidence on the FACT model has not been conducted since 2014. METHODS AND ANALYSIS: This study will follow the Joanna Briggs Institute's (JBI) methodology for scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. A structured search of several electronic databases (MedLine, CINAHL, PsycINFO, Psychology & Behavioural Sciences, Embase, Scopus, Sociological Abstracts and ASSIA Social Sciences Index Abstracts) will be conducted to locate relevant studies addressing models of care that adhere to the core components of the FACT model and that were published in English or Dutch from 2003 (model conception date) to the present day. To explore the range of populations served by FACT, we will not limit participant populations by age or diagnosis. With respect to FACT adaptations, we will include articles that explore modifications to the structure of FACT such as staffing complement, caseloads or interface with other health and social services. Articles identified from our structured searches will be screened independently by two reviewers. Data from included articles will be extracted, analysed and presented on tables and visual graphs, and summarised in a narrative report. ETHICS AND DISSEMINATION: Our scoping review does not require ethics approval as it does not involve human subjects and will draw evidence from published peer-reviewed articles. Our findings will be disseminated through journal publication, presentations at relevant conferences and distribution across our networks and those of our partners, including healthcare providers, researchers and other key stakeholders.
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 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 | 0.165 | 0.135 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.065 | 0.015 |
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".