Effects of three diet history questions on the amount of information gained from a sample of pet owners in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of 3 diet history questions on the amount and type of diet-related information gathered from pet owners and to assess whether diet-related information obtained with each question in person differed from information obtained with a diet history survey. SAMPLE: 99 pet owners. PROCEDURES: Participants' responses to 1 of 3 randomly selected diet history questions ("Tell me everything he [or she] eats throughout a day, starting first thing in the morning right through to the end of the day"; "What kind of food does she [or he] eat?"; or "What kind of foods does he [or she] eat?") were recorded and coded for analysis. Participants completed a postinteraction diet history survey. Amount and type of diet-related information obtained were compared among responses to the 3 diet history questions and between the response to each question and the diet history survey. RESULTS: The "Tell me…" question elicited a significantly higher total number of diet-related items (combined number of main diet, treat, human food, medication, and dietary supplement items) than did the "What kind of food…" or "What kind of foods…" questions. The diet history survey captured significantly more information than did the "What kind of food…" or "What kind of foods…" questions; there was little difference between results of the diet history survey and the "Tell me…" question, except that treats were more frequently disclosed on the survey. CONCLUSIONS AND CLINICAL RELEVANCE: Findings reinforced the value of using broad, open questions or requests that invite expansion from clients for gathering diet-related information.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".