Stability of an empirical psychosocial taxonomy across type of diabetes and treatment
Bibliographic record
Abstract
AIMS: The aims of the study were (i) to examine whether an empirical psychosocial taxonomy, based on key diabetes-related variables, is independent of type of diabetes and treatment, and (ii) to further establish the external validation of the taxonomy. METHODS: In a cross-sectional study, 82 patients with Type 1 and 86 patients with Type 2 diabetes mellitus were assigned to one of three psychosocial patient profiles based on their Multidimensional Diabetes Questionnaire (MDQ) scores. General psychological and diabetes-specific measures were obtained through self-report and HbA(1c) was measured. RESULTS: Equal proportions of Type 1 and Type 2 patients, and of patients using insulin and oral medication/diet only were classified within each of the three psychosocial profiles. External validation confirmed the validity and distinctiveness of the patients' profiles. The patient profiles were independent of demographic variables, body mass index, duration of diabetes, complexity of treatment, number of complications, social desirability, and major stress levels. CONCLUSIONS: The Psychosocial Taxonomy for Patients with Diabetes provides a new way to categorize individuals who may have more in common than just their type of diabetes and/or its treatment and can help target interventions to individual patients' needs.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".