What Types of Data are Pharmacy Education Scholars Using in Their Abstracts for Poster Presentations?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to describe the data being used to support poster presentations in pharmacy education scholarship. METHODS: Research and education posters presented at the 2020 American Association of Colleges of Pharmacy Annual Meeting were unitized to isolate text to be coded, and two coders categorized the quantitative and qualitative data by type and source. Questionnaires, instruments, and exams were categorized as new (ie developed and used for this particular inquiry) vs. existing. Qualitative data types were categorized as interviews, focus groups, self-reflections, analysis of student work products (eg lab reports assessed for student understanding), comments (ie written or verbal comments), and other (eg course reports). RESULTS: Two hundred and sixteen abstracts were included in the analysis, with 80 (37%) of abstracts relying on data derived from respondent's perceptions. Further, 143 abstracts (66%) used at least one new questionnaire, instrument, or exam. In 57% of the cases where multiple data sources were used, the study involved interprofessional education (eg multiple health professions learners) or pharmacy student-investigator combinations, and 28 abstracts (13%) did not use pharmacy students as a source. Less than 5% of all abstracts analyzed used traditional qualitative methods of interviews and focus groups. CONCLUSION: This study can open conversations around how to improve the quality of pharmacy education research and the identification of areas within the scholarship of teaching and learning that may benefit from improvement.
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 | 0.144 | 0.486 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".