Cognitive state, substance use patterns and outcome after discharge from Kfar Izun, a unique rehabilitation facility
Bibliographic record
Abstract
We studied cognitive performance following discharge from a novel rehabilitation facility, treating individuals with psychosis that developed during trips abroad following mandatory military service. Montreal Cognitive Assessment (MoCA), phonetic and semantic fluency, State-Trait Anxiety, and self-Efficiency were administered before discharge, and 3 and 6 months after discharge. Of the 43 participants (30.2% females), 23(54.8%) had cognitive impairment (MoCA <27), and 15(35.7%) had poor phonetic fluency. Anxiety trait and state were high and inversely correlated with self-efficacy (R¼-0.48, p¼0.001) and phonetic flu- ency (R¼-0.43, p¼0.004) and was higher among those who experienced physical exposure, females, and those who served in non-combat army units. Six months after discharge, of 32 participants, 28 were working/studying with a 58.1% reduction in smoking and alcohol con- sumption, and 16 participants stopped substance use. Phonetic fluency improved among the high anxiety state group with no change among the others. High anxiety levels lowered among those who were still using drugs after six months. The anxiety level lowered and 87.1% of the participants were conducting a productive lifestyle at 6 months after discharge, but half still abused cannabis. Bigger sample and longer follow up would be needed to learn more about the impact of rehabilitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".