Bad experiences in the hospital: the stories keep coming
Bibliographic record
Abstract
In this issue of the journal, two patients narrate stories about their disappointments with a healthcare system that did not seem to care about them as people. Michel Villette, a sociologist in France, tells the story of his hip surgery in an ‘elite’ French orthopaedic hospital.1 Melissa McCullough, a bioethicist and attorney, relates the story of her more prolonged ordeal with an uncommon neurological diagnosis in the National Health System in the UK.2 Despite these differences—undergoing a routine orthopaedic procedure in an exclusive private care setting versus diagnosing and managing a far from routine neurological problem in a public health system—these two stories share fundamental features. Both recount numerous failures of the system to deliver patient-centred care. Similar stories could be told about both the private and public healthcare systems in North America. And these stories are consistent with those of many other patients, including ones written by physicians when they (or their family members) have been patients,3–5 as well as accounts from patients, like the current authors, who, though not clinicians, have professional backgrounds that particularly equip them to identify and articulate deficiencies in healthcare delivery.6 7 Why do these stories of bad patient experiences continue to appear from every health care system? Broadly, these stories illustrate gaps in quality in the dimension of ‘patient-centred care’, one of the six dimensions of quality in the Institute of Medicine definition.8 Metrics of patient-centred care in the hospital setting, like the commonly used Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey,9 10 include several dimensions: communication with the nurses and physicians, the environment of the hospital (how clean is the hospital and how quiet is it at night), the experience of care (getting help when you need it; pain control) and planning for discharge. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".