Self-reported medical, medication and laboratory error in eight countries: risk factors for chronically ill adults
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors associated with self-reported medical, medication and laboratory error in eight countries. DESIGN: The Commonwealth Fund's 2008 International Health Policy Survey of chronically ill patients in eight countries. INTERVENTION: None. SETTING AND PARTICIPANTS: A multi-country telephone survey was conducted between 3 March and 30 May 2008 with patients in Australia, Canada, France, Germany, the Netherlands, New Zealand, the UK and the USA who self-reported being chronically ill. MAIN OUTCOME MEASURE: A bivariate analysis was performed to determine significant explanatory variables of medical, medication and laboratory error (P < 0.01) for inclusion in a binary logistic regression model. RESULTS: The final regression model included eight risk factors for self-reported error: age 65 and under, education level of some college or less, presence of two or more chronic conditions, high prescription drug use (four+ drugs), four or more doctors seen within 2 years, a care coordination problem, poor doctor-patient communication and use of an emergency department. CONCLUSION: Risk factors with the greatest ability to predict experiencing an error encompassed issues with coordination of care and provider knowledge of a patient's medical history. The identification of these risk factors could help policymakers and organizations to proactively reduce the likelihood of error through greater examination of system- and organization-level practices.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".