Outcomes of inquiry-based learning in health professions education: a scoping review
Bibliographic record
Abstract
Background: Open inquiry-based learning (IBL) that aims to foster higher-level thinking, is defined by students formulating their own questions and learning through exploration. The present study aimed to summarize the breadth of metrics used to evaluate health professions trainees in open IBL curricula. Methods: We conducted a scoping review to identify publications detailing trainee outcomes in open IBL initiatives in health professions education. We queried five databases and included studies which described interventions with five phases of IBL (orientation, conceptualization, investigation, conclusion, and discussion). We completed abstract and full text reviews in duplicate. Data were collated and summarized. Results: From 3030 record, 21 studies were included in the final extraction (k = 0.94), with nine involving physician trainees and twelve involving nursing trainees. Three studies used validated data collection tools to measure student inquiry behavior, and a single study used a validated data collection tool to measure critical thinking abilities. Most studies (n = 11) reported trainee self-reported satisfaction or perceived gain of skills as the primary outcome. All four studies using validated tools reported high scores in inquiry behaviors at the end of the curriculum and results on critical thinking skills were mixed. One study collected serial data, while remaining studies collected pre-post or post-only data. Conclusion: IBL has the potential to cultivate a climate of curiosity among health professions learners. However, studies have relied heavily on subjective outcomes. Limited studies reported standardized measures of inquiry behaviors suggest favorable results. Curriculum innovations using IBL could make use of existing tools to better understand their impact on students’ inquiry-oriented skills.
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 | 0.041 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".