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Record W2957027870 · doi:10.7861/clinmedicine.19-4-278

Physician engagement: the Vancouver Medical Staff Association engagement charter

2019· review· en· W2957027870 on OpenAlexaffabout
Simon W. Rabkin, Marshall Dahl, R. R. Patterson, Noa Mallek, Lynn Straatman, Andrew Pinfold, Marthe Charles, Stephen van Gaal, Sophia Wong, Himat Vaghadia

Bibliographic record

VenueClinical Medicine · 2019
Typereview
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsVancouver Coastal HealthVancouver Hospital and Health Sciences CentreUniversity of British ColumbiaVancouver General Hospital
Fundersnot available
KeywordsCharterMedicineHealth careConstruct (python library)InstitutionCommunity engagementSet (abstract data type)NursingMedical educationFamily medicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

Engagement of physicians with their healthcare community or institution should be a central issue in healthcare because it can be translated into improved patient care, enhanced well-being for physicians as well as safer, more effective and less costly healthcare. To accomplish the mission/goal of meaningful physician engagement, we set about to establish a 'charter' for physician engagement. We defined our concept of meaningful physician engagement and customised the engagement spectrum construct for physician relationship with their healthcare community or institution. While recognising the importance of physician leaders within the hierarchical system for efficacy of organisational management, relying only on physicians in formal executive positions is insufficient for developing physician engagement. There is a need for widespread physician engagement across the organisation. The objective is both an improvement in patient care and in physician well-being.

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)0.026
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.010
Insufficient payload (model declined to judge)0.0100.007

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 head0.355
GPT teacher head0.596
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations9
Published2019
Admission routes2
Has abstractyes

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