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Information overload in healthcare management: How the READ Portal is helping healthcare managers

2016· article· en· W2320954282 on OpenAlexvenueno aff
Alyssa Green

Bibliographic record

VenueJournal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada · 2016
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation overloadNegotiationHealth careProductivityInformation needsHealth informationQuality (philosophy)BusinessPatient portalKnowledge managementComputer scienceWorld Wide WebInformation systemPublic relationsInternet privacyPolitical science

Abstract

fetched live from OpenAlex

Abstract: Information overload is a serious threat to the productivity of healthcare managers. Instead of facilitating informed decision making, an overabundance of information actually impedes managers from negotiating information effectively. There are many methods of dealing with information overload, one of which is the use of web infomediaries as a source of information. The University of British Colummbia’s Centre for Health Care management’s READ Portal (http://www.read.chcm.ubc.ca) is an example of an infomediary that is striving to help healthcare managers overcome the effects of information overload. This portal aggregates content from numerous high-quality sources, which is then hosted in one easy to access location. Content is condensed into brief abstracts and synopses that are easy to ingest and includes links to full-text articles and papers that viewers can either choose to visit or not, depending on their needs. The READ Portal can be used as a model for other organizations looking to meet the information needs of managers without overwhelming them with excessive information.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.000
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 head0.006
GPT teacher head0.221
Teacher spread0.216 · 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
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

Citations11
Published2016
Admission routes1
Has abstractyes

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