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Record W3042487016 · doi:10.1177/0840470420935474

Shifting traditional healthcare paradoxes—The case for true system transformation

2020· article· en· W3042487016 on OpenAlexaffabout
Alan A. Monavvari, Lori Brady, Lisa Golec Harper, Parisa Mehrfar

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMarkham Stouffville HospitalNorth York General HospitalUniversity of TorontoRegional Municipality of DurhamToronto Public Health
Fundersnot available
KeywordsHealth careMeaning (existential)Ranking (information retrieval)Healthcare systemFunction (biology)Public relationsNinthBusinessPsychologySociologyProcess managementPolitical scienceComputer scienceLawArtificial intelligence

Abstract

fetched live from OpenAlex

Although national spending on healthcare has progressed on an upward trend over several decades, issues regarding performance remain. Challenges such as access to specialist care and maternal and infant mortality rates contributed to Canada's recent ranking of ninth among 11 Organisation for Economic Co-operation and Development countries for overall health system performance. Although disruptive transformation is required to resolve our chronic performance issues, effective change cannot be realized without addressing the foundational elements of patient-centred care, interprofessional care, and system integration. Inspired by examples of innovative disruption in other jurisdictions and industries, these three concepts are outlined as the core ingredients for healthcare transformation and describe how they currently function in a paradoxical manner-as self-contradictory statements which in reality are not executed to their true meaning. This article illustrates how improvements in health system performance are hinged to the need to rectify and fuse these three mutually inclusive and inseparable concepts.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.998

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.096
GPT teacher head0.408
Teacher spread0.312 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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
Published2020
Admission routes2
Has abstractyes

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