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Record W2358141489 · doi:10.1186/s12913-016-1416-4

Erratum to: Researching Complex Interventions in Health: The State of the Art

2016· erratum· en· W2358141489 on OpenAlexaff
Peter Craig, Ingalill Rahm-Hallberg, Nicky Britten, Gunilla Borglin, Gabriele Meyer, Sascha Köpke, Jane Noyes, Jackie Chandler, Sara Levati, Anne Sales, Lehana Thabane, Lora Giangregorio, Nancy Feeley, Sylvie Cossette, Rod S Taylor, Jacqueline Hill, David Richards, Willem Kuyken, Louise von Essén, Andrew James Williams, Karla Hemming, Richard Lilford, Alan Girling, Monica Taljaard, Munyaradzi Dimairo, Mark Petticrew, Janis Baird, Graham Moore, Willem Odendaal, Salla Atkins, Elizabeth Lutge, Natalie Leon, Simon Lewin, Katherine Payne, Theo vanAchterberg, Walter Sermeus, Martin Pitt, Thomas Monks

Bibliographic record

VenueBMC Health Services Research · 2016
Typeerratum
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de MontréalOttawa HospitalMontreal Heart InstituteMcGill UniversityJewish General HospitalUniversity of WaterlooMcMaster University
FundersNational Institute for Health and Care Research
KeywordsNursing researchHealth informaticsSection (typography)Martin lutherState (computer science)Public healthLibrary sciencePsychologyMedia studiesSociologyMedicineTheologyPhilosophyNursingComputer science

Abstract

fetched live from OpenAlex

Erratum to: Researching Complex Interventions in Health: The State of the Art. Available at http://hdl.handle.net/10871/23086

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)0.013
metaresearch head score (Gemma)0.173
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.100
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.173
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.008
Science and technology studies0.0060.004
Scholarly communication0.0080.007
Open science0.0040.004
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.1000.092

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 head0.713
GPT teacher head0.723
Teacher spread0.010 · 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
GenreEditorial

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

Citations1
Published2016
Admission routes1
Has abstractyes

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