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Record W3110520157

Perceived oral health and access to care among men with a history of incarceration.

2019· article· en· W3110520157 on OpenAlexaff
Leeann Donnelly, Ruth Elwood Martin, Mario Brondani

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPrisonFocus groupThematic analysisCriminal justiceQualitative researchImprisonmentHealth careEthnic groupMedicinePsychologyNursingCriminologySociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Objectives: To explore the perceptions of oral health and access to care experiences of men with a history of incarceration and to identify factors contributing to current oral health inequities within their community. Methods: , a qualitative data management program. Results: The participants ranged in age from 29 years to 69 years, came from a variety of ethnic backgrounds, and had different prison setting experiences. Five major themes emerged: not on the radar, stigma of incarceration, being shot down, caught in the system, and institutional conditioning. Conclusions: The personal backgrounds, experiences with health and dental care during prison time, and the unique challenges faced by men with a history of incarceration influenced their perceptions and their ability to access dental services. Dental professionals can help to change these perceptions and experiences by creating a safe space for these individuals to access and receive care comfortably.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.295
Teacher spread0.257 · 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 designObservational
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

Citations5
Published2019
Admission routes1
Has abstractyes

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