Role-Emergent Model: An Effective Strategy to Address Clinical Placement Shortages
Bibliographic record
Abstract
Purpose: To evaluate the effectiveness of an advanced pharmacy practice experience (APPE) delivered at “role-emergent” placement sites within long-term care (LTC) facilities that are preceptored by off-site community pharmacists.Method: Seven LTC facilities participated: five newly recruited test sites preceptored by off-site pharmacists who supervised students remotely (“role-emergent” placements), and two previously established hospital-based facilities with on-site pharmacists who provided continuous student supervision (“role-established” placements) as a comparison group. Students participated in pre-APPE training. Both qualitative and quantitative methods were used to obtain student learning performance on 13 pre-defined learning objectives and 21 indicators of site resources and skills-development opportunities. Structured open-ended feedback questions and reflective student observations elicited more personal and situational experiences. These combined with faculty reviews of student documentation of their patient care delivery encounters and LTC Staff perspectives enabled comparisons between the two APPE formats. Results: A total of 23 students participated: three at role-established and 20 at role-emergent sites. Evaluations indicated that all students successfully completed their learning objectives. Some differences were apparent – for example students at role-emergent sites expressed a desire for more one-to-one time with their pharmacy preceptor, but they also benefitted from more inter-professional collaboration and interacted with a broader range of health professionals than students with on-site APPE preceptors. Conclusions: This study demonstrated that equivalent but non-identical learning occurs at LTC locations with off-site preceptors (role-emergent) as in role-established hospital-based settings with on-site preceptors. Importantly, it also opens opportunities for many new APPE placement opportunities since there about three times as many LTC facilities as acute care hospitals in our jurisdiction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".