Involuntary Outpatient Commitment in Florida: Case Information and Provider Experience and Opinions
Bibliographic record
Abstract
The use of involuntary outpatient commitment (IOC) is a significant international issue. Variations can be found in Australia, New Zealand, Scotland, Ontario (Canada), Switzerland, and the United States. Its use varies considerably by country and in the United States, between states. In Florida, the IOC statute has been used sparingly. This paper first presents information about the first fifty IOC cases in Florida including a description of the pre- and post-IOC order emergency commitments and state hospital admissions of these individuals. It then provides results from a survey of mental health professionals about their experience with and opinions about IOC. The majority of the individuals with IOC orders had at least one emergency commitment in the two years pre-IOC order ( n = 46; range 1–7) and in the two years post-IOC order ( n = 41; range 1–13). While 41 individuals experienced 68 total emergency commitments in the 180 days prior to the IOC order, 18 individuals had 24 emergency commitments in the 180 days after the order. Eleven had at least one state hospital admission pre-IOC order, with eight having such an admission post-IOC order. Results from the survey suggest that a number of issues have reduced the use of IOC, including difficulties in applying the statute, inadequate clinical resources, and skepticism regarding the practical effect of an IOC order on positive clinical outcomes. The implications of these results for policy development are discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".