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Record W2953716124 · doi:10.1097/hcm.0000000000000272

The Best of Both Models

2019· article· en· W2953716124 on OpenAlexaffabout
Laurel Giasson

Bibliographic record

VenueThe Health Care Manager · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsOntario Shores Centre for Mental Health Sciences
Fundersnot available
KeywordsSession (web analytics)Key (lock)BusinessHealth careKnowledge managementPublic relationsProcess managementComputer sciencePolitical scienceComputer security

Abstract

fetched live from OpenAlex

Project management (PM) is considered an important competency for improving organizational efficiencies, and its practice is evolving in health care organizations. As a medium-sized health care organization, Ontario Shores Centre for Mental Health Sciences used a hybrid PM office model and created a PM network to connect the small, centralized PM office to the decentralized project leaders. The key components to create the PM network were (1) skilled leader, (2) organizational support, (3) infrastructure, and (4) session content. Membership grew from 12 to 45 members within the first year. The network continues to evolve to meet member and organizational needs.

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)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0120.014
Open science0.0040.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0830.041

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 head0.107
GPT teacher head0.463
Teacher spread0.356 · 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
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

Citations2
Published2019
Admission routes2
Has abstractyes

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