Pictograms for safer medication handling by health care workers: a validation study in nursing students in Poland
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Medication use often causes errors that are dangerous to the health of patients. Previous studies indicate that the use of pharmaceutical pictograms can effectively reduce medication errors. The purpose of this study was to determine the comprehensibility, representativeness, and recall rate of nine medication safety pictograms in a sample of nursing students in Poland in order to validate these images. METHODS: A pictogram validation study was conducted in two phases among nursing students at the Hipolit Cegielski State University of Applied Sciences, Gniezno, Poland. All experimental protocols were approved by the Children's Hospital of Eastern Ontario Research Ethics Board (REB Protocol No: 19/122X). All methods were carried out in accordance with relevant guidelines and regulations. In phase 1, the participants' first exposure to the pictograms, the students were asked to guess the meaning of the pictograms without any additional information in order to assess the pictograms' comprehensibility. To be considered valid, according to ISO standards, the pictograms had to be correctly understood by at least 66.7% of participants. After testing all pictograms, students were given explanations and meanings of the pictograms and asked to rate the representativeness of pictograms. To do so, participants were asked to select a number on a seven-point Likert-style scale to indicate the perceived strength of the relationship between the pictogram and its intended meaning for each pictogram. To be considered valid, a pictogram had to be rated at least five on this scale by at least 66.7% of participants. Phase 2 took place four weeks later, during which recall of the intended meaning and representativeness were assessed following the same procedure. RESULTS: A total of 66 third-year nursing students participated in both phases. In phase 1, of the nine pictograms, six met ISO requirements for comprehensibility and seven met ISO requirements for representativeness. In phase 2, all nine pictograms were correctly understood and rated at least 5 by at least 66.7% of participants. Therefore, all nine pictograms are considered valid. CONCLUSIONS: The nine medication safety pictograms can be deployed, but must be combined with training and a written hazard statement to improve comprehension.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".