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Is the oral health impact profile measuring up? Investigating the scale’s construct validity using structural equation modelling

2008· article· en· W2019962676 on OpenAlexaffabout
Sarah R. Baker, Barry Gibson, David Locker

Bibliographic record

VenueCommunity Dentistry And Oral Epidemiology · 2008
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConstruct (python library)Construct validityScale (ratio)Structural equation modelingConceptual modelMeasure (data warehouse)MedicinePsychometricsData miningStatisticsMathematicsComputer scienceClinical psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of the study was to provide an empirical test of the construct validity of the Oral Health Impact Profile as a measure of Locker's conceptual model of oral health. METHODS: A secondary analysis of data from the Ontario Study of Older Adults was carried out using structural equation modelling to assess the degree to which scale items measured the construct they were supposed to measure (within-construct validity) and whether relations between constructs were as hypothesized by Locker's model (between-construct validity). RESULTS: The findings indicated that the Oral Health Impact Profile as currently conceived does not have adequate within-construct validity. Scale items did not always measure the construct they were supposed to measure, some items within a construct were redundant, many measured more than one construct, and the scale did not represent seven separate constructs of oral health as originally devised. Following reconceptualization of the scale, the revised six-factor 22 item version was a better fit to the data. However, the scale did not have adequate between-construct validity. CONCLUSION: The present findings do not provide support for the conceptual basis of the Oral Health Impact Profile as a measure of Locker's model of oral health. The need for further conceptual development of the scale, and Locker's model, are discussed.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
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 head0.435
GPT teacher head0.445
Teacher spread0.010 · 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

Citations45
Published2008
Admission routes2
Has abstractyes

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