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Record W2911068218 · doi:10.1097/spc.0000000000000412

Best practices on team communication: interprofessional practice in oncology

2019· review· en· W2911068218 on OpenAlexaff
Laura D’Alimonte, Elizabeth McLaney, Lisa Di Prospero

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2019
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsCornerstoneExcellenceBest practiceKey (lock)MedicineKnowledge managementQuality (philosophy)Medical educationComputer scienceManagement

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Good communication is the cornerstone of interprofessional care teams providing optimized quality patient care. Over the last decade, advances in technology have provided tools to improve communication; however, opportunities still exist for innovation and implementation. RECENT FINDINGS: The literature suggests that interprofessional education and assessment of team communication are fundamental in supporting collaborative care. The literature favours an interactive, team-based approach (e.g. simulation) to learning about communication, in which communication competencies and behaviours are practiced explicitly in an open, feedback-rich environment. SUMMARY: Key elements of excellence in communication are embedded in three priority recommendations: first, the team must adopt a practice strategy that leverages accessible and timely communication second, the team must be open to initial and ongoing training within the domain of 'effective communication' third, communication must be the cornerstone to producing a high-performing team that will provide the best care possible.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.396
GPT teacher head0.652
Teacher spread0.256 · 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 designOther design
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

Citations8
Published2019
Admission routes1
Has abstractyes

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