Dependency, trust and choice? Examining agency and ‘forced options’ within secondary-healthcare contexts
Bibliographic record
Abstract
This article seeks to extend understandings of the ways in which trust is integral to analysing ‘choice’ within healthcare contexts, while also reappraising choice and its salience for grasping the nature of trust. Interrogating processes of ‘choosing to trust’, the authors describe various mechanisms through which ‘decisions’ are constrained while emphasising enduring agency to (dis)trust, even amid contexts where choice would appear annihilated by patients’ vulnerability. Drawing initially on Greener, Luhmann and Giddens, the article develops an analysis of how features of vulnerability, time and consciousness function in bounding choices and trust. Multiple structurations of choosing and trusting, alongside continuing agency, help further illuminate various power dimensions within clinical encounters. This theoretical analysis is illustrated using qualitative interview data from two studies across contrasting service settings in Australia and England, enabling recognition of further system and contextual influences upon patients’ vulnerability, dependency and trust, as these characterise processes of ‘choice’.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".