Identifying and Prioritizing Educational Needs of Female Adolescents in Relation to Healthy Eating Based on Analytic Hierarchy Process Model
Bibliographic record
Abstract
In order to better plan health based interventions, educators and health promoters need to make decisions in this regard. In the meantime, it should be noted that, multiple criteria decision making methods with theoretical roots and accuracy of forecasting results are less considered. The current study is a descriptive research carried out on 15 experts working in Yazd Health Centers using purposeful sampling. In order to identify wrong eating habits of students, Delphi method is used. In the next step, these habits are compared, one by one, and scored with Analytical Hierarchy Process (AHP) Model. In the end, data are analyzed using Expert Choice 11 software. Seven major wrong eating habits of female adolescents are identified: junk food consumption, drinking soda and sweet drinks, eating fast food, deleting main meals, improper diets, low intake of vegetables, and not eating breakfast. Among these, low intake of vegetables, eating fast food, and not eating breakfast, with weight rate of 32.4%, 19.4% and 19.3%, are specified as the first three priorities of education. In various fields of education and health promotion, including prioritizing training needs, employing techniques with potentials of assessing multiple criteria at the same time can be highly efficient.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".