Direction of wording and responses to items in oral health‐related quality of life questionnaires for children and their parents
Bibliographic record
Abstract
OBJECTIVES: In order to minimize acquiescence response set bias, it is often recommended that questionnaires measuring attitudes, behaviours or health states contain items worded positively and negatively. It has also been suggested that when measuring health status this approach means that both the negative and positive dimensions of health can be assessed. This study aimed at assessing the performance of negatively and positively worded items in questionnaires to measure child and parent perceptions of child oral health-related quality of life. METHODS: Both the child and parent questionnaire included four pairs of items, one negatively worded and one positively worded, that assessed eating, appearance, oral self-care and self-confidence. The response format was a five-point Likert frequency scale with a 'Don't know' option. Prior to analysis, the positive items were reverse coded. The relative performance of the two sets of items was assessed by means of comparisons of the proportions with 'Don't know' responses or missing values, mean item scores and proportions with the two highest frequency codes. Kappa statistics and intraclass correlation coefficients were used to assess the agreement between the negative and reverse-coded positive items and scores and the agreement between child and parent pairs. Factor analysis was used to determine if the two sets of items were measuring the same underlying construct. RESULTS: The study was completed by 91 Canadian children and 100 parents (91 child-parent pairs) recruited from clinics treating paediatric, orthodontic and oro-facial conditions. The positively worded items elicited substantially more 'Don't know' responses or missing values than the negatively worded items and failed to discriminate between groups. In addition, mean item scores and proportions with the highest frequency codes were substantially larger for reverse-coded positive than negative items. Agreement between pairs of items was slight. Child-parent agreement was substantial for a scale constructed from the negative items but only moderate for the positive items. Factor analysis revealed that the two sets of items loaded onto different factors. CONCLUSIONS: The performance of the positively worded items was unsatisfactory and their use in oral health-related quality of life indexes, either to reduce response set or assess positive oral health, is at best questionable.
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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 | 0.036 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".