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Record W2889060910 · doi:10.3399/bjgp18x698717

Books:<i>Look it up! What Patients, Doctors, Nurses, and Pharmacists Need to Know About the Internet and Primary Health Care</i>

2018· article· en· W2889060910 on OpenAlexaffabout
Manish Praful Ranpara

Bibliographic record

VenueBritish Journal of General Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWonderThe InternetMedicineNeed to knowPrimary careQueen (butterfly)Nurse practitionersPrimary health careGeneral practiceMedical educationHealth careNursingInternet privacyFamily medicineWorld Wide WebComputer sciencePsychology

Abstract

fetched live from OpenAlex

Pierre Pluye, Roland Grad, and Julie Barlow McGill-Queen’s University Press, 2017, HB , 200 pp , £16.99, 978-0773551367 Given the numerous resources designed to aid clinical decision making, I often wonder where to start looking for information on some occasions and when to stop on others. It is challenging to integrate best evidence in practice given the time restraints and limited resources in general practice. Look it up! is enlightening because it provides guidance on effectively finding answers …

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)0.003
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.456
Teacher spread0.408 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2018
Admission routes2
Has abstractyes

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