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Record W3134282505 · doi:10.1177/0840470421995934

Can facility-based transitional care improve patient flow? Lessons from four Canadian regions

2021· article· en· W3134282505 on OpenAlexafffundabout
Sara A. Kreindler, Ashley Struthers, Noah Star, Sarah Bowen, Stephanie Hastings, Shannon Winters, Keir Johnson, Sara Mallinson, Meaghan Brierley, Mohammed Rashidul Anwar, Zaid Aboud, Jenny Basran, Leah Goertzen

Bibliographic record

VenueHealthcare Management Forum · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of SaskatchewanRoyal University HospitalAlberta Health ServicesGeorge & Fay Yee Centre for Healthcare InnovationWinnipeg Regional Health AuthorityUniversity of Manitoba
FundersAlberta InnovatesMichael Smith Health Research BCInstitute of Health Services and Policy ResearchSaskatchewan Health Research FoundationResearch Manitoba
KeywordsUnit (ring theory)Process (computing)Intervention (counseling)Health careTransitional careProcess managementNursingPopulationMedicineOperations managementMedical emergencyBusinessPsychologyComputer scienceEnvironmental healthEngineeringPolitical science

Abstract

fetched live from OpenAlex

Units providing transitional, subacute, or restorative care represent a common intervention to facilitate patient flow and improve outcomes for lower acuity (often older) inpatients; however, little is known about Canadian health systems' experiences with such "transition units." This comparative case study of diverse units in four health regions (48 interviews) identified important success factors and pitfalls. A fundamental requirement for success is to clearly define the unit's intended population and design the model around its needs. Planners must also ensure that the unit be resourced and staffed to deliver truly restorative care. Finally, streamlined processes must be developed to help patients access and move through the unit. Units that were perceived as more effective appeared to have satisfactorily addressed these population, capacity, and process issues, whereas those perceived as less effective continued to struggle with them. Findings suggest principles to support optimal design and implementation of transition units.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.335
Teacher spread0.293 · 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.

Study designNot applicable
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

Citations8
Published2021
Admission routes3
Has abstractyes

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