<p>Gender Based Cognitive Determinants Of Medication Adherence In Older Adults With Chronic Conditions</p>
Bibliographic record
Abstract
BACKGROUND: Life course gender-role traits and social stereotypes could affect the pattern of medication adherence in old age. OBJECTIVES: The main purpose of this study was to investigate gender based cognitive determinants of medication adherence in a sample of older adults who suffer from at least a chronic condition. METHODS: In this cross-sectional design, 455 older adults participated from five health centers in Tabriz city, Iran from June to August 2017 using a random sampling method. Next, required data about medication adherence, knowledge and beliefs about prescribed medications, perceived self-efficacy in medication adherence, illness perception, and reasons for medication non-adherence were gathered using a structured written questionnaire through face-to-face interviews with the attendees. RESULTS: Low medication adherence was reported by 54.5% of the study participants. Perceived self-efficacy for medication adherence (OR = 1.04; 95% CI: 1.00, 1.08) and medication adherence reason (OR = 0.96; 95% CI: 0.92, 0.99) were two identified strong predictors of medication adherence among the studied older men. Illness perception (OR = 1.02; 95% CI: 1.00, 1.02) and beliefs toward prescribed medication (OR = 0.95; 95% CI: 0.93, 0.98) were both recognized as the significant predictors of medication adherence in the older women subgroup. CONCLUSION: Gender based variations were noted regarding the medication adherence in the studied sample and attributes of cognitive function were the main pinpointed elucidating parameters for the non-conformity. The explicit cognitive processes must be considered in care provision or interventional programs that target medication adherence in older adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".