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Record W2112023781 · doi:10.1177/2158244014539331

Dimensional Analysis of Psychosocial Barriers to Prevention of Early Childhood Caries Among Recent Immigrants

2014· article· en· W2112023781 on OpenAlexaff
Arnaldo Perez, Maryam Amin

Bibliographic record

VenueSAGE Open · 2014
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychosocialImmigrationLanguage barrierPsychologyDevelopmental psychologyFocus groupMedicineSociologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

The objective of this article is to define the underlying dimensions of psychosocial barriers to obtaining and providing dental care for young children among recent immigrants. Fifteen focus groups were conducted with 99 primary caregivers from African, South Asian, and Chinese recent immigrants. A secondary analysis of identified barriers using dimensional analysis methodology was performed to determine dimensions and properties of barriers. The analysis continued until irreducible properties were found or emerging dimensions were not relevant to the study. Identified dimensions were associated with barriers and individuals. Type, number, level, objectiveness, nature, and impact were barrier-related; awareness and controllability were individual-related dimensions. Type refers to barriers themselves. Number and level indicate the amount and location of barriers, respectively. Objectiveness refers to the extent that perceived barrier reflects reality and nature indicates its intrinsic characteristic. Impact concerns behaviors, goals, and outcomes compromised by barriers. Awareness alludes to the extent that individuals are aware of the barriers and controllability explains how much control people perceive to have over barriers. Identified dimensions are useful for better understanding and addressing existing barriers to children’s optimal oral health.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.315
Teacher spread0.303 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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