Brief Report: Does Your Patient Know Your Name? An Approach to Enhancing Patients' Awareness of Their Caretaker's Name
Bibliographic record
Abstract
This brief report determines whether patients admitted to a large teaching hospital knew the name of their caretaker (physician or nurse) and whether emphasis on patients' awareness of this name improved their recall. A survey of 100 patients on the internal medicine and neurology services at a large teaching hospital in BrookLyn, NY, was conducted. A derivative survey was also conducted on 30 different patients to see whether caretaker name recall was enhanced after the patients were advised of the importance of remembering this name. Of patients initially tested, 14.7% correctly stated their physician's name, and 21.3% correctly stated their nurse's name (p < 0.3). After being given the name of their physician in writing and being asked to remember it, 76.2% of a different group of patients correctly stated their physician's name. Less than a quarter of the patients initiaLLy surveyed were able to state either their physician's or nurse's name. However, after a specific effort to have a smaller group of patients remember their physician's name, more than 75% did so. Therefore, it was concluded that simple interventions such as providing the patients with their physician's name in writing and emphasizing the importance of knowing it result in a significantly greater percentage of physician-name recall.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".