Bibliographic record
Abstract
Media influence on suicide Television programme makers have an ethical responsibilityEditor-We agree with Hawton and Williams that training courses for careers in the media offer the potential for improved portrayal of suicide in the media. 1 The media, however, clearly can affect many facets of health related behaviour.We recently reported the effect of the death from cervical cancer of a character (Alma) in the television soap opera Coronation Street on the NHS cervical screening programme in the north west of England. 2 3 Our studies showed an excess of 14 000 cervical smear tests performed as a result of the storyline (a 21% increase on the previous year), although only 2000 of them were in women whose test was overdue or who had had no previous smear test.The remaining 12 000 smear tests were performed on women attending for an early, unscheduled test or who were due a smear test anyway and brought their appointment forward.The large increase in the number of smear tests led to a strain on local laboratories, with the time taken to report results increasing to beyond acceptable quality assurance limits-a factor likely to provoke excess anxiety in women.We also found that many women were prompted to attend for a cervical smear test because the storyline made them worry.This anxiety generating approach contrasts with current initiatives to encourage women to make an informed choice about screening.Television programme makers should realise the power of such stories not only to achieve maximal viewing figures but also to cause fear and anxiety, as well as the consumption of scarce healthcare resources.Those responsible for promoting health need to engage programme makers in a full ethical debate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".