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Record W3082029388 · doi:10.1177/0840470420952485

Shared care in surgery: Practical considerations for surgical leaders

2020· article· en· W3082029388 on OpenAlexaff
Morgann Reid, Alex Lee, David R. Urbach, Craig Kuziemsky, Morad Hameed, Husein Moloo, Fady Balaa

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of British ColumbiaMacEwan UniversityUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsPromotion (chess)Shared careIsolation (microbiology)Health carePandemicNursingMedicineBurnoutBusinessProcess managementPublic relationsCoronavirus disease 2019 (COVID-19)DiseasePolitical sciencePrimary careFamily medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The recent COVID-19 pandemic has highlighted limitations in current healthcare systems and needed strategies to increase surgical access. This article presents a team-based integration model that embraces intra-disciplinary collaboration in shared clinical care, professional development, and administrative processes to address this surge in demand for surgical care. Implementing this model will require communicating the rationale for and benefits of shared care, while shifting patient trust to a team of providers. For the individual surgeon, advantages of clinical integration through shared care include decreased burnout and professional isolation, and more efficient transitions into and out of practice. Advantages to the system include greater surgeon availability, streamlined disease site wait lists, and promotion of system efficiency through a centralized distribution of clinical resources. We present a framework to stimulate national dialogue around shared care that will ultimately help overcome system bottlenecks for surgical patients and provide support for health professionals.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.176
GPT teacher head0.395
Teacher spread0.219 · 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 designNot applicable
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

Citations8
Published2020
Admission routes1
Has abstractyes

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