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Record W2766396138 · doi:10.12927/cjnl.2017.25255

Driving it Home: Leading with an Interprofessional Collaborative Team Approach in Home Care

2017· letter· en· W2766396138 on OpenAlexaffvenueabout
Cynthia J Bergeron, Gina Barton, Wendy Gamache-Holmes, Mary Ellen Barry, Barbara Butler, Lisa Dunnett, William Koval, Kristen Augustin, Natalie Russell, Monica Tominey

Bibliographic record

VenueNursing leadership · 2017
Typeletter
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHorizon Health Network
Fundersnot available
KeywordsHome healthNursingInterprofessional educationHealth careNursing homesPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

As a prime example of the value of an interprofessional approach to care advocated by Orchard and colleagues earlier in this issue (Orchard 2017a, 2017b), the following case study profiles one highly effective interprofessional New Brunswick-based team which cares for clients and families in their homes; a model which has been functional and extremely successful for more than three decades and remains unparalleled in Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0160.007
Scholarly communication0.0070.008
Open science0.0030.006
Research integrity0.0300.036
Insufficient payload (model declined to judge)0.0030.001

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 head0.136
GPT teacher head0.426
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes3
Has abstractyes

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