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Record W4404516786 · doi:10.1016/j.ajpe.2024.101331

Assessment of Student-Reported Preparedness in Leadership and Professional Service Management Post Capstone Course

2024· article· en· W4404516786 on OpenAlexaff
Tatiana Makhinova, Meng Deng

Bibliographic record

VenueAmerican Journal of Pharmaceutical Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsMartec (Canada)University of Alberta
Fundersnot available
KeywordsPreparednessCapstone courseCourse (navigation)Medical educationCapstoneService (business)PsychologyMedicinePedagogyEngineeringPolitical scienceComputer scienceCurriculumBusinessComputer security

Abstract

fetched live from OpenAlex

OBJECTIVE: Our objective was to explore the effectiveness and utility of a newly introduced capstone course for developing leadership and management skills in pharmacy students. METHODS: A secondary analysis was conducted on precourse and postcourse questionnaire data collected from third-year doctoral-level students and their mentors. The frequency and mean scores of responses to statements regarding confidence in leadership, management, and collaboration skills scored on the Likert scale were presented. Open-ended comments from students and mentors were also grouped and summarized. RESULTS: For all questionnaire items related to the confidence self-assessment, the majority of students responded either agree (40%-69% per question) or strongly agree (6%-56% per question). Students also showed a significant increase in confidence in their management skills after completing the course. Student comments and feedback regarding the course were grouped into 3 major categories: content, timeline/organization, and group dynamics. CONCLUSION: Based on feedback from students and mentors, the capstone course is a valuable learning experience for pharmacy students and an effective tool for developing leadership, management, and collaboration skills. The value of in-person learning, engaged mentors, and real-world relevance for the capstone project is highlighted.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.176
GPT teacher head0.580
Teacher spread0.404 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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