Is the oral health impact profile measuring up? Investigating the scale’s construct validity using structural equation modelling
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".