An Infographic Assignment to Translate Self-Care Therapeutics into Practical Application
Bibliographic record
Abstract
INNOVATION: An infographic assignment was developed and integrated into an advanced self-care therapeutics elective course in a School of Pharmacy to facilitate practical communication of dynamic and innovative approaches to patient care while supporting diversity in assessment. DESCRIPTION: The 'Spotlight on Self-Care' assignment required pharmacy students to develop three infographic deliverables detailing comprehensive care for one minor ailment. The three deliverables were: 1) a magazine insert for healthcare professionals, 2) a patient-friendly handout, and 3) a pharmacists' companion practice tool. All deliverables were assessed by rubrics using consistent criteria, including: clinical content, logical presentation, research quality, visual elements and formatting. The five highest-scoring magazine inserts were offered publication in the Pharmacy Practice and Business Magazine after peer review. CRITICAL APPRAISAL: The submitted infographics put clinical content from the course into action by leveraging recent trends in effective communication. As an assessment, the infographic assignment recognized a unique profile of skills in the students that was statistically different from the profile of skills that was evaluated by the multiple-choice examinations. The key issues to address include reducing grading time requirements and developing strategies to detect copyrighted materials. Future investigations into the nature of the skills gained by the students through the exercise, as well as their perceptions regarding the professional value of the exercise, are important for refining the administration of this assignment.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.018 |
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".