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Record W4224282198 · doi:10.1044/2022_persp-21-00277

Integrating Oral Health in Speech-Language Pathology Practice: A Viewpoint

2022· article· en· W4224282198 on OpenAlexaff
Rebecca Affoo, Shauna Hachey

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineScope of practiceOral healthHealth careScope (computer science)SwallowingAshaFamily medicineNursingDentistryLinguisticsComputer science

Abstract

fetched live from OpenAlex

Purpose: The purpose of this article is to propose and outline the role that speech-language pathologists (SLPs) have in oral health care. Additional goals include describing oral health, describing the relationships between oral health and overall health, and providing information about oral health assessment and management practices, as well as resources appropriate for use by SLPs. Conclusions: Oral health is critical to overall health and a marker of health equity. An alarming number of people in the United States, such as those living on a low income, experience significant barriers to accessing oral health care. SLPs work to prevent, assess, diagnose, and treat communication and swallowing disorders for clients across the lifespan. Part of this scope of practice includes the evaluation and management of oral health to varying degrees. Integrating basic oral health care into speech-language pathology practice has the potential to improve oral health equity for several priority populations. Supplemental Material: https://doi.org/10.23641/asha.19606288

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)0.013
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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.013
Scholarly communication0.0100.009
Open science0.0020.007
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.445
Teacher spread0.389 · 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
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

Citations5
Published2022
Admission routes1
Has abstractyes

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