‘Waiting for’ and ‘waiting in’ public and private hospitals: a qualitative study of patient trust in South Australia
Bibliographic record
Abstract
BACKGROUND: Waiting times for hospital appointments, treatment and/or surgery have become a major political and health service problem, leading to national maximum waiting times and policies to reduce waiting times. Quantitative studies have documented waiting times for various types of surgery and longer waiting times in public vs private hospitals. However, very little qualitative research has explored patient experiences of waiting, how this compares between public and private hospitals, and the implications for trust in hospitals and healthcare professionals. The aim of this paper is to provide a deep understanding of the impact of waiting times on patient trust in public and private hospitals. METHODS: A qualitative study in South Australia, including 36 in-depth interviews (18 from public and 18 from private hospitals). Data collection occurred in 2012-13, and data were analysed using pre-coding, followed by conceptual and theoretical categorisation. RESULTS: Participants differentiated between experiences of 'waiting for' (e.g. for specialist appointments and surgery) and 'waiting in' (e.g. in emergency departments and outpatient clinics) public and private hospitals. Whilst 'waiting for' public hospitals was longer than private hospitals, this was often justified and accepted by public patients (e.g. due to reduced government funding), therefore it did not lead to distrust of public hospitals. Private patients had shorter 'waiting for' hospital services, increasing their trust in private hospitals and distrust of public hospitals. Public patients also recounted many experiences of longer 'waiting in' public hospitals, leading to frustration and anxiety, although they rarely blamed or distrusted the doctors or nurses, instead blaming an underfunded system and over-worked staff. Doctors and nurses were seen to be doing their best, and therefore trustworthy. CONCLUSION: Although public patients experienced longer 'waiting for' and 'waiting in' public hospitals, it did not lead to widespread distrust in public hospitals or healthcare professionals. Private patients recounted largely positive stories of reduced 'waiting for' and 'waiting in' private hospitals, and generally distrusted public hospitals. The continuing trust by public patients in the face of negative experiences may be understood as a form of exchange trust norm, in which institutional trust is based on base-level expectations of consistency and minimum standards of care and safety. The institutional trust by private patients may be understood as a form of communal trust norm, whereby trust is based on the additional and higher-level expectations of flexibility, reduced waiting and more time with healthcare professionals.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".