Exposing Pharmacy Students to Challenges Surrounding Care of Young Children via a Novel Role-Emerging Placement
Bibliographic record
Abstract
Embedding opportunities for undergraduate pharmacy students to move between academic and practiceenvironments is key to transform their perception of patient care and to facilitate learning of the skills required forthe changing profession (Smith and Darracott, 2011). An approach adopted by many health care professions toprepare students for diversity with their field, is exposure to non-workplace environments in the form ofrole-emerging placements (REPs) (Whiteford and Wright St-Clair, 2002).The study presented is part of an ongoing action research project; this cycle focusses on exposing students tochallenges surrounding care of young children. Barriers and facilitators arising from an earlier pilot of REPs in theCardiff School of Pharmacy were considered when designing and implementing innovative placements for entrylevel pharmacy undergraduates in venues where mother and toddler groups were running. Students participated in apre-placement workshop where they explored a flexible list of questions to facilitate their interactions. Placementswere supervised by members of staff, who supported students throughout their experience and during a group debriefat the end of each session. Students were called to reflect further during a post-placement workshop with the rest oftheir colleagues.The full cohort of students submitted a copy of their overall reflections. Entries were analysed via thematic analysisto provide an overview. The sessions raised awareness of issues when providing pharmaceutical care to children andcontributed to students’ professional development. Challenges to their interactions were identified and suggestionsfor improvement were made. Results will inform structure and content of future REPs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".