An analysis, using concept mapping, of diabetic patients' knowledge, before and after patient education
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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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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 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".