Derivation and validation of the short version of the Malaysian Oral Health Impact Profile
Bibliographic record
Abstract
OBJECTIVES: This paper describes the development of a short version of the Malaysian Oral Health Impact Profile. METHODS: The 45-item OHIP(M) was shortened using a method known as the 'item frequency method'. Here, the two most frequently reported items from each of the seven OHIP(M) subscales were chosen to form the short version, designated as the S-OHIP(M). Field testing was conducted to assess the effect of different modes of administration (mail versus interview) of the short form and to test its measurement properties (reliability and validity). A total of 206 respondents completed the questionnaire. In order to carry out test-retest analysis, a second administration was carried out 15 days after the first administration on a selected subsample. RESULTS: The mail questionnaire had a lower response rate and a higher percentage of missing data than the interview administered questionnaire. However, the mail mode of administration resulted in higher scores than interview. Cronbach's alpha was 0.89 and the ICC was also 0.89. All hypotheses developed to assess validity were confirmed. CONCLUSION: The S-OHIP(M) was found to be valid and reliable and appropriate for use in the cross-sectional studies in Malaysian adult populations.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".