A demonstration study comparing “role-emergent” versus “role-established” pharmacy clinical placement experiences in long-term care facilities
Bibliographic record
Abstract
BACKGROUND: Increasing challenges to recruit hospital sites with full-time on-site pharmacy preceptors for institutional-based Advanced Pharmacy Practice Experiences (APPE) has made it necessary to consider alternate experiential models. Sites with on-site discipline specific preceptors to supervise students have typically been referred to in the literature as "role-established" sites. In British Columbia, long-term care (LTC) facilities offered a unique opportunity to address placement capacity issues. However, since the majority of these facilities are serviced by off-site community pharmacists, this study was undertaken to explore the viability of supervising pharmacy students remotely - a model referred to in the literature as "role-emergent" placements. This paper's objectives are to discuss pharmacy preceptors' and LTC non-pharmacist staff experiences with this model. METHODS: The study consisted of three phases: (1) the development phase which included delivery of a training program to create a pool of potential LTC preceptors, (2) an evaluation phase to test the viability of the LTC role-emergent model with seven pharmacists (two role-established and five role-emergent) together with their LTC staff, and (3) expansion of LTC role-emergent sites to build capacity. Both qualitative and quantitative methods were used to obtain feedback from pharmacists and staff and t-tests and Mann-Whitney U tests were used to examine equivalency of survey outcomes from staff representing both models. RESULTS: The 76 pharmacists who completed the training program survey rated the modules as "largely" meeting their learning needs. All five role-emergent pharmacists and 29 LTC participating staff reported positive experiences with the pharmacy preceptor-student-staff collaboration. Preceptors reported that having students work side-by-side with facility staff promoted inter-professional collaboration. The staff viewed students' presence as a mutually beneficial experience, suggesting that the students' presence had enabled them to deliver better care to the residents. As a direct result of the study findings, the annual role-emergent placement capacity was increased to over 45 by the end of the study. CONCLUSIONS: This study demonstrated that role-emergent LTC facilities were not only viable for quality institutional APPEs but also provided more available sites, greater student placement capacity, and more trained pharmacy preceptors than could be achieved in role-established facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".