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Record W3034301108 · doi:10.1111/hir.12319

Health information professionals: delivering core services and value in extraordinary times

2020· article· en· W3034301108 on OpenAlexaboutno aff
Frances Johnson

Bibliographic record

VenueHealth Information & Libraries Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipBannerPublic relationsCoronavirus disease 2019 (COVID-19)PandemicValue (mathematics)Library sciencePolitical scienceMedicineMedical educationComputer science

Abstract

fetched live from OpenAlex

The 2020 virtual issue of the Health Information and Libraries Journal (HILJ) is published to link to the CILIP Health Libraries Group Conference which was to take place in Scotland 22-25th July. Whilst the conference was postponed in light of the coronavirus (COVID-19) pandemic, its themes of (i) Working in Partnership; (ii) Resilience and Well-being; (iii) Public and Patient involvement; (iv) Quality Impact and Metrics; and (v) Improvement and Innovation have nevertheless provided the basis on which to compile this virtual issue. Overarching these themes is a core value of the HIL profession, to provide relevant, timely and sustainable information services, and the articles selected from HILJ (2018 through to March 2020) contribute to the aim of meeting and going beyond these goals under the conference banner of 'not your average day in the office'. The virtual issue mirrors the format of a regular issue of HILJ, a review article, four original articles and three from our regular features: 'Dissertations into Practice', 'International Perspectives and Initiatives' and 'Teaching and Learning in Action'. The authors come from Canada, China, Croatia, Sweden and the UK. All articles included in this issue are available online.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.038
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.398
Teacher spread0.332 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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