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Record W2801162336 · doi:10.1176/appi.ps.201700468

Trends in Results of HBIPS National Performance Measures and Association With Year of Adoption

2018· article· en· W2801162336 on OpenAlexaboutno aff
Kenneth A. Rasinski, Stephen Schmaltz, Scott C. Williams, David W. Baker

Bibliographic record

VenuePsychiatric Services · 2018
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsSeclusionMedicineQuarter (Canadian coin)CommissionHealth services researchInpatient careDemographyHealth careFamily medicineEmergency medicinePsychiatryPublic healthNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: Multiple studies demonstrate a consistent pattern of improvement on quality measures among health care organizations after they begin collecting and reporting data. This study compared results on psychiatric performance measures among cohorts of hospitals with different characteristics that elected to begin reporting on the measures at various points in time. METHODS: Quarterly reporting of Hospital-Based Inpatient Psychiatric Services (HBIPS) measures to the Joint Commission was used to examine trends in performance among four hospital cohorts that began reporting in 2009 (N=243), 2011 (N=139), 2014 (N=137), or 2015 (N=372). The HBIPS measures address admission screening, restraint and seclusion use, justification of use of multiple antipsychotic medications, and discharge planning. Comparisons were based upon initial quarters of data reported and change rates. RESULTS: After adjustment for covariates, the analyses showed that all cohorts significantly improved across quarters for admission screening, justification of multiple antipsychotic medications, and discharge planning. Restraint hours significantly dropped over the initial reporting periods, but only for the 2009 and 2015 cohorts. Seclusion hours significantly dropped over the six reporting periods for all cohorts except 2011. CONCLUSIONS: Several differences were observed across cohorts in the rate of change between baseline and final measurement for various measures. In nearly every case, however, hospitals that began reporting measurement data earlier performed better than subsequent cohorts during the later cohorts' first quarter of reporting.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.332
Teacher spread0.308 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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