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Reliability and convergent and discriminant validity of the Child Oral Health Impact Profile (COHIP Child’s version)

2007· article· en· W2056136155 on OpenAlexaboutno aff
Hillary L. Broder, Maureen Wilson‐Genderson

Bibliographic record

VenueCommunity Dentistry And Oral Epidemiology · 2007
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCronbach's alphaIntraclass correlationDiscriminant validityConvergent validityQuality of life (healthcare)Clinical psychologyPsychometrics

Abstract

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OBJECTIVES: The purpose of the current study was to assess the reliability as well as the convergent and discriminant validity of the Child Oral Health Impact Profile (COHIP). The questionnaire consisted of five domains that assessed oral health, functional well-being, social-emotional well-being, school environment, and self-image. COHIP was designed to measure self-reported oral health-related quality of life (OHRQoL) of children between ages 8 and 15 years old, using both positively and negatively worded items. METHODS: Children were recruited from pediatric, orthodontic, and craniofacial clinical settings in the USA and Canada. A comparison group of children not seeking dental treatment was recruited from two US elementary schools. Participants included 157 pediatric, 152 orthodontic and 110 patients with craniofacial anomalies, and 104 community-based participants. Scale reliability was assessed with Cronbach's alpha coefficient. Retest reliability was examined by intraclass correlation and paired t-test for a subset of participants who did not report a health change. Discriminant validity was assessed in two ways: (i) the COHIP scores of the four groups of children (three clinical and one community-dwelling) were compared by anova and (ii) for two of the clinical groups, the association between COHIP scores and clinical indices was calculated. Convergent validity was examined using partial Spearman correlations between COHIP scores and Global Health Ratings controlling for demographic variables. RESULTS: The children (n = 523) averaged 11.6 years (SD = 1.60); 51.6 % were female; and represented diverse ethnicities (black = 22.4%, Latino = 32.1%, white = 35.1%, other 10.4%). Overall COHIP scores ranged from 28 to 135 (mean +/- SD, 99.0 +/- 19.2) for the children. Scale reliability for the overall COHIP was excellent: Cronbach's alpha coefficient = 0.91 for the overall score. The test-retest reliability of the overall COHIP was also excellent (ICC = 0.84) and there was no statistically significant shift in scores over time. Discriminant validity was supported by significant differences (P = 0.003 overall COHIP) among the three clinical groups: the craniofacial group reported the lowest overall COHIP quality of life scores of the clinical groups. Within the pediatric dental group, children with greater dental decay reported lower COHIP scores suggesting a lower OHRQoL (r = -0.26, P = 0.02) and within the orthodontic group, children with larger overjet reported lower COHIP scores (r = -0.25, P = 0.005). Controlling for the effect of the participants' age, gender, and ethnicity, the association between the overall COHIP score and Global Health rating was statistically significant (P < 0.05) and similar in strength for the three clinical groups (pediatric dental = 0.29, orthodontic = 0.23, and craniofacial = 0.24) and highest for the community group (0.36). CONCLUSION: The overall COHIP showed excellent scale reliability overall and test-retest reliability. Both discriminant and convergent validity of the COHIP were supported by the comparisons among and within the four groups of children. Further testing will examine the utility of the instrument in both clinical and epidemiological samples.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.092
GPT teacher head0.405
Teacher spread0.314 · 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.

Study designObservational
DomainMethods
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

Citations210
Published2007
Admission routes1
Has abstractyes

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