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Record W2014415522 · doi:10.1097/hcm.0b013e3181a2cb63

Health Care Globalization

2009· article· en· W2014415522 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Health Care Manager · 2009
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsFowler Kennedy Sport Medicine Clinic
Fundersnot available
KeywordsOutsourcingGlobalizationVirtual teamHealth careBusinessPublic relationsMedical tourismFace (sociological concept)Global healthConfusionKnowledge managementMarketingPolitical sciencePsychologyComputer scienceSociology

Abstract

fetched live from OpenAlex

As health care organizations expand and move into global markets, they face many leadership challenges, including the difficulty of leading individuals who are geographically dispersed. This article provides global managers with guidelines for leading and motivating individuals or teams from a distance while overcoming the typical challenges that "virtual leaders" and "virtual teams" face: employee isolation, confusion, language barriers, cultural differences, and technological breakdowns. Fortunately, technological advances in communications have provided various methods to accommodate geographically dispersed or "global virtual teams." Health care leaders now have the ability to lead global teams from afar by becoming "virtual leaders" with a responsibility to lead a "virtual team." Three models of globalization presented and discussed are outsourcing of health care services, medical tourism, and telerobotics. These models require global managers to lead virtually, and a positive relationship between the virtual leader and the virtual team member is vital in the success of global health care organizations.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.397

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 studies0.0000.000
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 head0.011
GPT teacher head0.350
Teacher spread0.339 · 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