Issues and developments on the consumer recovery construct
Bibliographic record
Abstract
The consumer recovery model has had increasing influence on mental health practices in the United States, Western Europe, and several other countries. However, adoption of the model has reflected political decisions rather than empirical evidence of the validity of the model or its value for treatment services. The recovery construct is poorly defined, and until recently there has been no reliable and valid measure with which to base a research program. We have developed an empirical measure that is well-suited for both research and clinical applications: the Maryland Assessment of Recovery in Serious Mental Ill-ness (MARS). We briefly describe the MARS and present preliminary data demonstrating that recovery is not a simple by-product of traditional outcome do-mains, but seems to be a distinct construct that may have important implications for understanding consumers with serious mental illness and for evaluating the outcome of treatment programs.
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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.022 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".