Using guided inquiry and the information search process to develop research confidence among first year anatomy students
Bibliographic record
Abstract
With the growing volume of obtainable medical information and scientific literature, it is crucial that students in the field of allied health professions develop and refine the research skill set necessary to effectively find, retrieve, analyze, and use this information. This skill set can be effectively developed using student inquiry; an active learning process where students answer questions using research and data analysis. Therefore, with the pedagogical goal of developing information literacy among a cohort of allied health professional trainees, first year students studying human anatomy completed inquiry-based projects that were structured within the framework of the Information Search Process. This article thoroughly describes the conceptualization, creation, improvement, implementation, and assessment of the projects beginning with version one, the Student Inquiry Projects. Following a pilot of the Student Inquiry Projects various evidence-based improvements resulted in the final project version called the Inquiry Guided Learning Projects (IGLPs). A full assessment of the IGLPs revealed that students' self-perceived confidence improved for all tested research skills including: research question development, research question selection, exploration of peer-review literature, acquisition of resources, effective communication of results, and literature citation (all P < 0.05). Furthermore, six months following project completion students retained improved confidence in research question development and effective communication of results, with 90% of students indicating the IGLPs were directly responsible for these improvements. By guiding students through the Information Search Process, the IGLPs successfully developed research confidence among allied health trainees.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".