Psychological, emotional and social impairments are associated with adherence and healthcare spending in type 2 diabetic patients: an observational study.
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to assess the association among anxiety, depression, stress, social support and emotional abilities with adherence and healthcare spending in type 2 diabetic patients. PATIENTS AND METHODS: Sixty-four patients were enrolled and completed: Interpersonal Processes of Care (IPC), 20-item Toronto Alexithymia Scale (TAS-20), Rapid Stress Assessment Scale (RSAS), Morisky Medication Adherence Scale (MMAS-4), International Physical Activity Questionnaire (IPAQ)-Short Form and a socio-anamnestic questionnaire regarding also the healthcare spending. RESULTS: Mathematical linear regressions models were performed showing the predictive effects of: anxiety and social support scores (RSAS) on adherence levels (respectively p =. 019; p =. 016); adherence levels on anxiolytic use (p =.04); aggressiveness scores (RSAS) on the number of general check-ups (p =.031); TAS-20 and physician-patient communication (IPC) on the number of hospitalization days (respectively p=.001; p=.008); physician patient decision making (IPC) scores on physical activity (IPAQ) levels (p=.025); physical activity (IPAQ) on the number of medical examinations (p=.039). CONCLUSIONS: An association among psychosocial impairment, adherence and healthcare spending was found. Future studies should investigate the effect of a brief psychological intervention in increasing adherence levels and reducing the healthcare spending in this clinical population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".