A qualitative study of patient (dis)trust in public and private hospitals: the importance of choice and pragmatic acceptance for trust considerations in South Australia
Bibliographic record
Abstract
BACKGROUND: This paper explores the nature and reasoning for (dis)trust in Australian public and private hospitals. Patient trust increases uptake of, engagement with and optimal outcomes from healthcare services and is therefore central to health practice, policy and planning. METHODS: A qualitative study in South Australia, including 36 in-depth interviews (18 from public and 18 from private hospitals). RESULTS: 'Private patients' made active choices about both their hospital and doctor, playing the role of the 'consumer', where trust and choice went hand in hand. The reputation of the doctor and hospital were key drivers of trust, under the assumption that a better reputation equates with higher quality care. However, making a choice to trust a doctor led to personal responsibility and the additional requirement for self-trust. 'Public patients' described having no choice in their hospital or doctor. They recognised 'problems' in the public healthcare system but accepted and even excused these as 'part of the system'. In order to justify their trust, they argued that doctors in public hospitals tried to do their best in difficult circumstances, thereby deserving of trust. This 'resigned trust' may stem from a lack of alternatives for free health care and thus a dependence on the system. CONCLUSION: These two contrasting models of trust within the same locality point to the way different configurations of healthcare systems, hospital experiences, insurance coverage and related forms of 'choice' combine to shape different formats of trust, as patients act to manage their vulnerability within these contexts.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".