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Record W6986961008

A Review of System-Level and Practitioner Relevant Factors That Improve Access to Primary Dental Care for Children Living in Vulnerable Contexts

2015· other· en· W6986961008 on OpenAlexaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingPsychological interventionPrimary careInvestment (military)Dental careHealth careResource (disambiguation)Early childhood caries
DOInot available

Abstract

fetched live from OpenAlex

The distribution of childhood caries and dental surgery in BC and Canada is inequitable and is a potential indicator of limited access to prevention and treatment through primary dental care. This paper focuses on different ways to improve access to primary dental care, specifically with an interest in better meeting the needs of children 0-18 years of age living in vulnerable contexts. Findings from this research indicate that policies and investment strategies that reduce the cost of primary dental care have significant impact on improving access, and that a combination of interventions that address economic, safety and health human resource related barriers to care can contribute. Innovative funding and staffing models, inter-disciplinary collaboration, the use of technology and transport, child centered and friendly care and targeted training, recruitment and retention strategies all contribute to increasing access. This paper recommends continued advocacy for policy changes to include primary dental care for children and youth as a part of the Canada Health Act as well as local planning that looks at innovative, creative, flexible and family-friendly ways of providing service, building staffing and maximizing the usage of existing infrastructure and resources.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.011
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.254
Teacher spread0.231 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2015
Admission routes1
Has abstractyes

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