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Record W2610341986 · doi:10.1186/s12913-017-2281-5

‘Waiting for’ and ‘waiting in’ public and private hospitals: a qualitative study of patient trust in South Australia

2017· article· en· W2610341986 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Health Services Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsUniversity of Waterloo
FundersFlinders University
KeywordsNursing researchHealth administrationHealth informaticsPublic healthMedicineQualitative researchHealth services researchNursingHealth economicsFamily medicineSociologySocial science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.323
GPT teacher head0.585
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it