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Record W2938729244 · doi:10.5195/jmla.2019.497

Implementation of a fee-based service model to university-affiliated researchers at the University of Alberta

2019· article· en· W2938729244 on OpenAlexaffabout
Janice Y. Kung, Thane Chambers

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of AlbertaImpactAlberta Health Services
Fundersnot available
KeywordsService (business)Presentation (obstetrics)SustainabilityBusinessService delivery frameworkEngineering managementService modelMedical educationKnowledge managementComputer scienceMarketingMedicineEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: There is growing demand for specialized services in academic libraries, including supporting systematic reviews and measuring research impact. STUDY PURPOSE: The John W. Scott Health Sciences Library implemented a fee-based pilot project for the Faculty of Nursing for one year to test a fee-based model for specialized services, to evaluate its sustainability and scalability for the longer term, and to assess the feasibility of extending this service model to other health sciences faculties. CASE PRESENTATION: We describe the development and delivery of the fee-based service model. Through a team-based approach, we successfully provided specialized services including mediated literature searching, research support, and research impact analyses to the Faculty of Nursing. DISCUSSION: Despite some challenges in developing and implementing the fee-based service model, our pilot project demonstrated demand for fee-based specialized services in the health sciences and suggests potential for this unique service model to continue and expand.

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)0.001
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 score0.169
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.414
Teacher spread0.340 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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