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Record W2005296552 · doi:10.1080/14999010903199340

Involuntary Outpatient Commitment in Florida: Case Information and Provider Experience and Opinions

2009· article· en· W2005296552 on OpenAlexaboutno aff
Annette Christy, John Petrila, Mark McCranie, Vivian A. Lotts

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2009
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsStatuteOrder (exchange)MedicineEmergency departmentFamily medicineMedical emergencyPolitical scienceLawPsychiatryBusiness

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.398
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2009
Admission routes1
Has abstractyes

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