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Record W2034723921 · doi:10.3163/1536-5050.99.4.008

A survey of librarians with a health sciences background

2011· article· en· W2034723921 on OpenAlexaboutno aff
Rebecca Raszewski

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary scienceMEDLINEData scienceWorld Wide WebComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Examinations of the experiences of librarians with a health sciences background have appeared in the library literature over the past decade. Fikar's 2001 survey was perhaps the first article written about this topic. The majority of respondents to Fikar's survey felt that their background was beneficial or would be beneficial to their librarianship career [1]. Watson's survey of a small sample of Canadian academic health sciences librarians found that the 21 of 30 (70%) respondents felt that having a health sciences degree was not very important or not at all important to a health sciences librarian career [2]. As opposed to this, a 2007 survey of British health librarians found that those with a science background said their degree “gave them more confidence with the terminology and general subject background.” Those without a health sciences degree emphasized “transferable skills or other management skills” as important rather than their background. Most felt there was a need for “better subject knowledge” [3]. The current survey sought to identify why those librarians who had a health sciences background chose librarianship and if they felt their health sciences background was advantageous in working as a health sciences librarian.

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)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.245
GPT teacher head0.452
Teacher spread0.208 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

Explore more

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