Your Goals, My Goals, Our Goals: The Complexity of Coconstructing Goals with Learners in Medical Education
Bibliographic record
Abstract
Phenomenon: Despite a long-standing recognition of the importance of learning goals in feedback, there has been relatively little research on how to address mismatches between learner goals and preceptor goals in medical education. Our study addresses this gap by reporting on challenges and strategies around goal coconstruction as identified by clinical educators who were learning and attempting to implement a goal-oriented feedback approach in their own teaching contexts. Approach: We employed a qualitative, design-based research methodology to study how 5 clinician educators incorporated goal-oriented feedback into their teaching practice. Participants attended workshops on goal-oriented feedback and reflective writing. They then narratively reflected over a 6-month period on their attempts with goal-oriented feedback and shared these reflections in periodic facilitated group discussions. Themes were developed using iterative thematic analysis of group discussions and individual exit interviews. Findings: Participants identified several benefits of goal setting in all environments. They perceived improved rapport with learners and developed empathy for the vastness of learner goals. However, they experienced several struggles especially when learner and preceptor goals did not match. These included (a) how to address learner goals that were not easily amenable to a coconstruction, (b) how to coconstruct goals while actively running a clinical practice, and (c) how to remain learner centered while raising preceptor goals based on perceived gaps. Insights: Mismatches between learner and preceptor goals are inevitable and frequent. Preceptors must find ways to coconstruct goals with learners in various learning environments. That said, in enacting goal-oriented feedback, preceptors are effectively using feedback throughout the teaching and learning interaction to coconstruct the learning environment, resulting in improved rapport with learners and emphasizing why it is important to focus feedback around goals.
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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 | 0.046 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".