Flexible ACT & Resource-group ACT: Different Working Procedures Which Can Supplement and Strengthen Each Other. A Response#
Bibliographic record
Abstract
This article is a response to Nordén and Norlander's 'Absence of Positive Results for Flexible Assertive Community Treatment. What is the next approach?'[1], in which they assert that 'at present [there is] no evidence for Flexible ACT and… that RACT might be able to provide new impulses and new vitality to the treatment mode of ACT'. We question their analyses and conclusions. We clarify Flexible ACT, referring to the Flexible Assertive Community Treatment Manual (van Veldhuizen, 2013) [2] to rectify misconceptions. We discuss Nordén and Norlander's interpretation of research on Flexible ACT. The fact that too little research has been done and that there are insufficient positive results cannot serve as a reason to propagate RACT. However, the Resource Group method does provide inspiration for working with clients to involve their networks more effectively in Flexible ACT.
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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.075 | 0.032 |
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".