EFFECTIVENESS OF A SELF-MANAGEMENT SUPPORT PROGRAM FOR TYPE 2 DIABETES MELLITUS IN PUBLIC HEALTH CENTERS IN BANGKOK, THAILAND: RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
African American grandparents are more likely than others to provide full-time care for their grandchildren; those providing full-time care are in poorer health than non-caregivers.Low medication adherence contributes to worse health outcomes for people with chronic medical conditions, and adherence is lower in blacks than whites.Patients' beliefs about medications and communication with prescribers and pharmacists have been shown to affect adherence.Semistructured interviews were held with 22 African American custodial grandmothers ages 40 or older with 1+ chronic health conditions.Questions on beliefs about medication, perceptions of patient-provider communication and barriers/facilitators to adherence were asked.Overprescribing or impersonal prescribing practices by providers was a major concern.Distrust of the medical establishment, including pharmaceutical companies was also expressed.Although participants with negative views agreed to the usefulness of medication, some chose to skip or discontinue prescribed medicines without informing providers.Media influence, particularly direct-to-consumer advertising, and concerns about side effects were sources of negative attitudes.On the other hand, participants often stated that medications were essential to their health, and adherence was seen as a positive way to maintain health; staying healthy in order to care for dependent children was a strong incentive for adherence.Daily routines and reminder devices helped participants take their medicines as prescribed.Participants were grateful to pharmacists who helped them with information on keeping medication costs low.Despite suspicions about motives of the pharmaceutical industry and prescribers, mature African American caregivers understand the importance of medication use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".