Bibliographic record
Abstract
The Twitter and Facebook accounts of the nursing community are fired up after the premier of MTV's new reality series, Scrubbing In, debuted a few weeks ago.A Jersey Shore version of nursing, Scrubbing In has enraged nurses from around the globe with its misguided representation of the nursing profession.Barbara Mildon (2013), president of the Canadian Nursing Association, which represents 150,000 registered nurses from across Canada, wrote a letter to MTV stating, "Scrubbing In's dramatized account of nurses' lives trivializes the critical work they perform.All of their hard work, from studying and gaining experience, to answering nursing's call, will be overshadowed by typical 'reality' show fodder."Dianne Martin (2013), executive director of the Registered Practical Nurses Association of Ontario, representing more than 38,000 RPNs throughout the province, also wrote, "I could tell you that, as a nurse, I'm insulted by the show's stereotypical characterization of nurses.I could tell you that stereotypes are ignorant, demeaning and damaging.I could tell you that the caricature of the 'sexy nurse' is outdated and worn out."Nurses are indeed fuming at MTV's portrayal of their work.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.035 |
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".