Exploring Adolescents’ Awareness of Diabetes: Using the Free Association Technique
Bibliographic record
Abstract
PURPOSE: Healthy adolescents' awareness of diabetes was explored, and gender and grade-level differences in understanding were determined. METHODS: Adolescents without diabetes in grades five, eight, and 10 (n=128) at four New Brunswick schools wrote down all words/expressions that came to mind when they heard the word "diabetes" (i.e., they used the free association technique). Answers were classified into categories using content analysis. RESULTS: Eighty-eight girls and 40 boys completed the activity (n=44, 52, and 32 in grades five, eight, and 10, respectively). Nine principal categories were identified: 66% of the adolescents cited sugar (e.g., eating too much sugar, not enough sugar), 48% treatment (e.g., needles, injections), 45% the nature of diabetes (e.g., a disease, types of diabetes, heredity), 41% nutrition (e.g., diet, sugar-containing foods, other foods), 38% blood (e.g., too much/not enough sugar in blood), 18% complications (e.g., death), 11% physiological manifestations/symptoms (e.g., fainting), 6% obesity, and 6% physical activity. No differences were found in category citation frequency between boys and girls and grade levels, except that grade 10 students more frequently cited the categories of treatment, blood, and obesity (p=0.05). CONCLUSIONS: Students thought of diabetes in terms of sugar and injections. Words related to obesity, prevention, and complications were cited infrequently.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".