Bibliographic record
Abstract
Abstract This article examines how older adults make decisions about their medications through interconnected axes of trust that operate across social networks. Trust is negotiated by older adults enrolled in a deprescribing programme which guides them through the process of reducing medications to mitigate risks associated with polypharmacy. Habermas’ work on the significance of communicative action in negotiating trust within social relationships informs our analysis, specifically in-depth semi-structured interviews with older adults about their medication use and the role of social networks in managing their health. Participants were age 70+ and experiencing polypharmacy. Our analysis discusses the social nature of medication practices and the importance of social networks for older adults’ decision-making. Their perspective reflects the critique of late-modern society put forward by Habermas. Negotiating trust in pharmaceutical decision-making requires navigating tensions across and between system networks (health-care professionals) and life-world networks (family and friends). This study contributes to our knowledge of how distinct forms of trust operate in different social spheres, setting the context for the way health-care decisions are made across social networks. Our analysis reinforces the need for older adults to engage meaningfully in health-care decision-making such that a convergence between system-world and life-world structures is encouraged. This would improve deprescribing programmes’ efficacy as older adults optimise their medication use and improve overall quality of life.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".