Bibliographic record
Abstract
OBJECTIVE: To develop a standard taxonomy for inpatient complaints that could be adopted in a wide array of health service institutions. DESIGN: A taxonomy was developed by merging the coding schemes from eight prior studies of patient complaints, and then by revising the received coding scheme in light of the codes and clarifications that emerged from a content analysis of patient complaints. SETTING: Two Boston area hospitals. PARTICIPANTS: Stratified random sample of 1216 complaints from patients in 2004. INTERVENTION: s) None. Main outcome measure(s) Patient complaints codes, provider codes and inter-rater reliability. RESULTS: A taxonomy comprising 22 patient complaint codes and five provider codes was developed. Inter-rater agreement for complaint codes was good (median Kappa statistic 0.66, interquartile range 0.55-0.80). Four codes were each used in more than 10% of the patient complaints filed: unprofessional conduct (19%); poor provider-patient communication (17%); treatment and care of patient (16%); and, having to wait for care (11%). Of the coding for the profession of the person complained about, 47% of the patient complaints were about staff in general or did not specify a particular profession; 22% identified a physician or dentist; 12% nursing staff; 11% administrative or support staff and 8% allied clinical health professionals. CONCLUSIONS: Standardized coding of patient complaint data may provide an opportunity for quality improvement, patient satisfaction and changes in patient care.
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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 | 0.030 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".