When patients should seek medical care for minor ailments: Perspectives of first- and final-year pharmacy students
Bibliographic record
Abstract
Background: Universities are tasked with preparing students to assist the public in managing minor ailments. This study aimed to determine when pharmacy students would refer patients to medical care as an indicator of clinical skill. Methods: First- and final-year students from four schools were surveyed to determine referral timelines for 17 scenarios. Responders also quantified symptom severity and their confidence levels. Results: Students responding to at least three cases were kept for analysis (n = 117). First-year students considered nasal congestion to be low in severity, with painful urination and rectal bleeding deemed more serious, all while considering most cases more serious than upper-year students. Student confidence was generally lower in new students. Referral times showed similar patterns between years and universities. Red eye, painful urination, diarrhoea (child), and Gastro-Oesophageal Reflux Disease (GORD) (unhealthy patient) were referred quicker than nasal allergies and cough. Referrals typically stayed within a two-week window for most situations. Conclusion: Timelines for medical care were similar between years and institutions. As expected, new students assessed cases as more serious and had less confidence than their upper-year colleagues. A concern for the institutions might be the low rate of real-world case exposure within programmes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".