An analysis, using concept mapping, of diabetic patients' knowledge, before and after patient education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study was designed to assess whether concept maps used with diabetic patients could describe their cognitive structure, before and after having followed an educational programme. Ten diabetic patients, in Paris and Geneva, were interviewed and, during the interview, a concept map was drawn up by the researcher, using the patient's words. This was done on three different occasions: the first day of the educational programme (Pre-evaluation), the last day (Post 1) of a week of education, then 3 to 4 months after education (Post 2). Twenty-eight maps were analysed, using a grid that quantified and qualified the knowledge expressed (knowledge categories, concept links, exactitude) and the organization of that knowledge (hierarchization of concept, cross-links). The examples shown in the maps of the 10 patients gave an illustration of how knowledge was developed or maintained with education, and also showed some learning difficulties encountered by the patients, the changes or preservation of their beliefs and the patients' preoccupations. This study shows that concept maps can be a suitable technique to explore the type and organization of the patients' prior knowledge and to visualize what they have learned after an educational programme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it