Perceptions of Medical Errors in Cancer Care
Bibliographic record
Abstract
OBJECTIVE: To analyze the print news media's coverage of sentinel events involving cancer patients. METHODS: Using LexisNexis, we identified English-language newspaper articles covering medical errors in cancer care between January 1, 2000, and December 31, 2010. Articles were coded for 3 major themes using a standardized abstraction instrument: narrative statements and point of view most prominently represented, attribution of blame, and orientation toward patient safety. We also abstracted country where the newspaper was published, type of error event, and extent of patient harm. RESULTS: We analyzed 64 articles from 37 print newspaper syndications that circulated in 6 countries/regions. Reports of medical errors rarely were framed from the point of view of a safety expert or the responsible clinician (13% and 3%, respectively) compared with the patient and legal points of view (both 30%). Articles held individual clinicians (41%) and hospital systems (28%) responsible for most errors. Four in 10 articles failed to present medical errors as "systems" problems. Article perspective varied considerably by country, with 53% of articles from the UK and 63% from Australia and New Zealand judged as negatively slanted compared with 14% in the United States and Canada. CONCLUSIONS: In reports of medical errors involving cancer patients, the news media regularly blame individual clinicians for mistakes and fail to present a systems-based understanding of these events.
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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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".