MétaCan
Menu
Back to cohort
Record W3027606513 · doi:10.1007/s40520-020-01579-z

Defining the characteristics of intermediate care models including transitional care: an international Delphi study

2020· article· en· W3027606513 on OpenAlexaff
Duygu Sezgin, Rónán Ó’Caoimh, Mark O’Donovan, Mohamed A. Salem, Luz López Samaniego, Cristina Arnal, Rafael Rodríguez‐Acuña, Marco Inzitari, Teija Hammar, Claire Holditch, Janet Prvu Bettger, Martin Vernon, Áine Carroll, Felix Gradinger, Gastón Perman, Martin Wilson, Antoine Vella, Antonio Cherubini, Helen Tucker, Maria Pia Fantini, Graziano Onder, Regina Roller‐Wirnsberger, Luis Miguel Gutiérrez‐Robledo, Matteo Cesari, Magdalena Kieliszek, Wilma van der Vlegel-Brouwer, Michelle Nelson, Leocadio Rodríguez‐Mañas, Eleftheria Antoniadou, François Barrière, Sebastian Lindblom, Grace Park, Isidoro Francisco Sánchez Pérez, Dolores Alguacil, Douglas Lowdon, María E. Alkiza, Cristina Alonso Bouzón, John Young, Ana Carriazo, Aaron Liew, Anne Hendry

Bibliographic record

VenueAging Clinical and Experimental Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsFraser HealthLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersEuropean Commission
KeywordsDelphi methodInterchangeabilityHealth careComparabilityPsychological interventionNursingDelphiTransitional carePoint of careService (business)MedicinePsychologyComputer scienceBusinessPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.045
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.084
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.003
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.313
GPT teacher head0.623
Teacher spread0.310 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations79
Published2020
Admission routes1
Has abstractno

Explore more

Same venueAging Clinical and Experimental ResearchSame topicInterprofessional Education and CollaborationFrench-language works237,207