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Record W4313434672 · doi:10.1007/978-3-031-07999-3_12

Sustainability: The Need to Transform Oral Health Systems

2022· book-chapter· en· W4313434672 on OpenAlexaff
Brett Duane, James Coughlan, Carlos Quintonez, Bridget Johnston, Julian Fisher, Eleni Pasdeki-Clewer, Paul Ashley

Bibliographic record

VenueBDJ clinician's guides · 2022
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSustainabilityWorkforceCorporate governanceBusinessPublic relationsKnowledge managementPolitical scienceFinanceComputer scienceLaw

Abstract

fetched live from OpenAlex

This chapter explores the role of system transformation in the drive towards sustainability. This chapter presents this broad vision of sustainability in dentistry by coupling sustainability with dentistry’s social contract, or the mutual commitments and reciprocal obligations that dentistry shares with society by virtue of being a regulated health profession. Healthcare systems need to be more focussed on efficient, appropriate treatment (based on evidence) to not only become more efficient but also to reduce their overall environmental impact (Hensher, et al., Health Economics, Policy and Law 15:419–439, 2020). A number of system characteristics need to be considered from an environmental, fiscal, and social perspective. The chapter views the lens of six building blocks for health systems strengthening; service delivery, health workforce, health information systems, access to essential medicines, financing, and leadership/governance. Sustainability isn’t about reducing your environmental emissions; it needs a system reconstruct; and all of these building blocks are pivotal in building a sustainable dental system.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0090.007
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.003

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 head0.465
GPT teacher head0.483
Teacher spread0.018 · 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 designTheoretical or conceptual
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

Citations2
Published2022
Admission routes1
Has abstractyes

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