Medication adherence, medication beliefs and social supportamong illiterate and low-literate community-dwelling olderadults with polypharmacy
Bibliographic record
Abstract
analysis, D -data interpretation, E -Manuscript preparation, F -literature search, G -funds CollectionBackground. polypharmacy can be an area of concern for the older population.a set of internal and external factors determine the degree of adherence to treatment in older adults.Objectives.We aimed to investigate whether medication belief and social support, taking into account the role of socio-demographic and clinical factors, are predictors of medication adherence among illiterate, low-literate community-dwelling older adults with polypharmacy.Material and methods.a cross-sectional study was conducted in the health centres of tabriz-iran in 2022.the data was collected using the socio-demographic and clinical questionnaires, Morisky, Green and levine's adherence scale, belief about Medicines Questionnaire (bMQ) and the Multidimensional scale of perceived social support (Mspss).hierarchical Multiple linear regression analysis was used to identify medication adherence predictors based on a conceptual framework.Results. the final sample size was 318 people.the results showed that age, education years, medication satisfaction, side effects of medications, doctor checkups, medication belief and social support were significant predictors of medication adherence.the necessity part of medication belief had a negative significant relationship, and the concern part had a positive significant relationship with medication adherence.Conclusions.a strong belief along with sufficient social support could be a good predictor of medication adherence.the results showed that elementary education has a positive relationship with people's medication adherence, even among low-literacy populations.the development of literacy movement programmes within communities to promote primary education among illiterate older adults is recommended.our findings also highlight the importance of improving patient-physician communication skills and clear communication in the formation of patients' behaviour.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".