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The Origins and Design of the Dental Pipeline Program

2005· article· en· W2167408418 on OpenAlexaff
Howard L. Bailit, Allan J. Formicola, Kim Herbert, Judith Stavisky, George Zamora

Bibliographic record

VenueJournal of Dental Education · 2005
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsCommunity Based Research Centre
Fundersnot available
KeywordsUnderrepresented MinorityEndowmentDental educationMedical educationPipeline (software)MedicineFamily medicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Funded by The Robert Wood Johnson Foundation and the California Endowment and with student financial aid from the W.K. Kellogg Foundation, the primary goal of the Pipeline, Profession, and Practice: Community-Based Dental Education program is to reduce disparities in access to dental care. In a national competition, fifteen dental schools were selected to participate. By the final year (2007) of the five-year project, the schools are expected to achieve three objectives: 1) increase the time (sixty days/year) that senior students and residents spend in patient-centered community clinics and practices treating underserved populations; 2) provide didactic and clinical courses for students and residents that prepare them for their community experiences; and 3) recruit more underrepresented minority and low-income students. The national program office that directs the project is located at Columbia University, and a national advisory committee oversees the program for the sponsoring organizations. The challenge is to demonstrate that the Pipeline objectives are achievable and that the program is sustainable without external support.

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)0.010
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0040.004
Open science0.0020.008
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0220.002

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 head0.035
GPT teacher head0.459
Teacher spread0.423 · 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 designQualitative
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

Citations58
Published2005
Admission routes1
Has abstractyes

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