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Record W3133609634 · doi:10.1080/14659891.2021.1879288

Sense of coherence and oral health of users of psychoactive substances

2021· article· en· W3133609634 on OpenAlexaboutno aff
Liliane de Oliveira Miranda, Andréa Neiva da Silva, Inara Pereira da Cunha, Fábio Luíz Mialhe, Karine Laura Cortellazzi, Valéria Rodrigues de Lacerda

Bibliographic record

VenueJournal of Substance Use · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialMedicineLogistic regressionOral healthOrdered logitToothacheQuarter (Canadian coin)Substance useClinical psychologyDentistryPsychiatryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Background: The aim of this study was to evaluate the factors associated with the sense of coherence (SOC) and oral health conditions among users of psychoactive substances.Methods: Data were obtained from 131 individuals accompanied by the Alcohol and Drug Psychosocial Care Center located at Brazil. The dependent variable was SOC, measured by means of the SOC 13 scale. Independent variables included sociodemographic conditions, access to dental services and presence of pain of dental origin. In addition, the participants’ caries experience was evaluated by the mean of the Decayed, Missing and Filled Teeth (DMFT) index.Results: As regards oral conditions, the mean DMFT index was 15.62 and the missing component represented the highest prevalence. Over half of the participants sought dental treatment for caries or treatment of pain recently and over a quarter of the sample reported dental pain in the last six months. Over half of the individuals had low SOC levels and the multiple logistic regression model showed that low scores on the SOC 13 scale were associated with substance users aged over 41 and female gender (p < .05).Conclusions: In conclusion, substance users had deteriorated oral health and low SOC levels. It is important to reinforce the SOC combined with the access to oral health services.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.054
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.120
GPT teacher head0.455
Teacher spread0.335 · 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 teacher head, 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

Citations0
Published2021
Admission routes1
Has abstractyes

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