P075: Discovering the unknown: using storytelling to identify emergent learning needs for the intrinsic competencies within an online needs assessment
Bibliographic record
Abstract
Introduction: Free Open Access Medical education (FOAM) resources have been developed using various needs assessment methods. We describe a storytelling exercise used to identify unperceived medical expert learning needs, which also resulted in the emergence of unknown learning needs within intrinsic physician roles. Methods: A FOAM curriculum was created for thrombosis based on an online needs assessment comprised of a topic listing, case scenarios, and a storytelling exercise. In the storytelling exercise, learners described i) a difficult case in thrombosis, and ii) why that case was difficult. In this qualitative description study, we performed a secondary thematic analysis of this storytelling data, coded for CanMEDS 2015 intrinsic roles. Two investigators independently coded transcripts to iteratively generate a coding framework. Results: 143 respondents completed the storytelling exercise. All responses yielded a gap in medical expertise, while 25 (17.5%) described an additional intrinsic theme. Learning needs in all six intrinsic roles were identified. The most commonly cited learning needs were in the Leader (recognizing how resource allocation impacts healthcare), Communicator (communicating expert knowledge with patients), and Collaborator (unclear communication between providers) domains. Participants who described an intrinsic learning need were primarily from emergency medicine (21/25, 84.0%). These excerpts were notable for how they expressed the complexity and affective components of medicine. Conclusion: Storytelling exercises can highlight context, attitudes, and relationships which provide depth to needs assessments. These narratives are a novel method of capturing emergent learning needs, which may be unknown to learner and faculty (Johari window). These intrinsic learning needs may ultimately be used to enrich learner-centered curricula.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".