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Record W2985169313 · doi:10.11124/jbisrir-2016-003263

Emergency department interventions for persons with dementia presenting with ambulatory care-sensitive conditions: a scoping review protocol

2017· review· en· W2985169313 on OpenAlexaff
Beverley Temple, Preetha Krishnan, Bev OʼConnell, Lyle George Grant, Lisa Demczuk

Bibliographic record

VenueThe JBI Database of Systematic Reviews and Implementation Reports · 2017
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsSaskatchewan PolytechnicWinnipeg Regional Health AuthorityUniversity of Manitoba
Fundersnot available
KeywordsEmergency departmentPsychological interventionStaffingMedicineDementiaAmbulatoryAmbulatory careProtocol (science)Medical emergencyNursingHealth careAlternative medicineDisease

Abstract

fetched live from OpenAlex

Review question/objective: The objective of this scoping review is to examine and map, within existing literature, the characteristics of emergency department/urgent care interventions, strategies or contextual factors, implemented to reduce unnecessary hospitalization of people with dementia (PWD) presenting at the emergency department/urgent care with ambulatory care-sensitive conditions (ACSC). More specifically, the review questions are: What non-pharmacological interventions or strategies, including, but not limited to, screening, assessments, clinical pathways, appropriate referrals and sensory overload reduction, are used in emergency departments for PWD presenting with ACSC? What are the characteristics and settings of these interventions, and how do they affect the disposition of PWD? What contextual factors, including, but not limited to, staff education, staffing mix and levels, and alterations to the physical environment, exist in emergency department/urgent care? What are the characteristics of these factors and the settings in which they are used?

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.139
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
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 head0.378
GPT teacher head0.581
Teacher spread0.203 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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