Perceptions of Medical Errors in Cancer Care
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.
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.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it