MétaCan
Menu
Back to cohort

Infectious Diseases: Career Preparation

2001· article· en· W2419028065 on OpenAlexaff
Allan Ronald, Ziad A. Memish

Bibliographic record

VenueJournal of Chemotherapy · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInfectious disease (medical specialty)MedicineGlobal healthPolitical scienceEconomic growthDiseasePublic healthNursingPathology

Abstract

fetched live from OpenAlex

The human resources for the discipline of Infectious Diseases are inadequate in many countries. There is no global definition of "Infectious Diseases" physicians and cover competency. Preparation for a career in this speciality varies greatly. In large populations in Asia and Africa, few individuals exist who have been trained to be Infectious Disease Clinicians. There is a great need by national and international societies to embrace this speciality and address the global deficiencies in this discipline by directing funding agencies as well as training institutions to redirect resources to strengthen the capacity of health professionals to deal with infectious diseases adequately throughout the world.

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)0.015
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: none
Teacher disagreement score0.081
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.001
Scholarly communication0.0060.002
Open science0.0010.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0810.045

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 head0.034
GPT teacher head0.422
Teacher spread0.388 · 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

Citations6
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of ChemotherapySame topicPrimary Care and Health OutcomesFrench-language works237,207