Window to the Unknown: Using Storytelling to Identify Learning Needs for the Intrinsic Competencies Within an Online Needs Assessment
Bibliographic record
Abstract
INTRODUCTION: Needs assessments are important for developing online educational resources, but they frequently do not capture learning needs in the intrinsic physician competencies. Storytelling exercises, by promoting critical reflection and emphasizing values and context, may assist curriculum developers in identifying emergent knowledge gaps in these areas that are initially unknown to learners. METHODS: that case was difficult. In this qualitative descriptive study, we performed a secondary thematic analysis of this storytelling data, coded for the CanMEDS 2015 intrinsic roles. Two investigators independently coded transcripts with iterative comparison. RESULTS: A total of 143 respondents completed the storytelling exercise. All responses yielded a gap in medical expertise, while 25 (17.5%) described an additional intrinsic role. Learning needs in all six intrinsic roles were identified. The most commonly cited learning needs were in the leader (recognizing how resource allocation impacts health care), communicator (communicating knowledge with patients), and collaborator (unclear communication between providers) roles. These excerpts were notable for how they expressed the complexity and affective components of medicine. CONCLUSIONS: Storytelling exercises can highlight context, attitudes, and relationships that provide depth to needs assessments. These narratives are a novel method of identifying gaps in intrinsic physician competencies that are initially unknown by learners (Johari window). These emergent intrinsic learning needs may be used to enrich learner-centered curricula.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".