Navigating discourses of feedback: developing a pattern system of feedback
Bibliographic record
Abstract
Although feedback is often presented as if it were a well-understood concept in health professions education, in practice it can mean many things. For some, feedback is a conversation about defining and improving performance, while for others it is the information generated by assessments and tools. Indeed, feedback has variously been defined as a process, as data, as a conversation, and as a reflective exercise. As a result, for a concept so central to what educators do, 'feedback' is ambiguous and has multiple meanings. Pattern theory affords opportunities to examine what scholars and practitioners mean when they use the term 'feedback'. Elaborating feedback as a pattern system can connect otherwise disjointed discourses of feedback. In this paper, the authors describe the development of a pattern system of feedback in medical education. Arksey & O'Malley's 5-stages of scoping reviews were adapted to enact a 6-step pattern system development methodology that included (1) Identifying the research question and scope of inquiry; (2) elaborating a strategy for pattern identification; (3) study selection; (4) abductive pattern representation development; (5) pattern system testing; and (6) summarizing and reporting the results. A pattern system of feedback was developed based on review of 218 full text articles and testing against an additional 2833 citations. This pattern system is made up of 36 pattern representations organized under 6 domains: feedback referent, feedback intentions, feedback information, feedback processing, feedback response, and feedback meta. The pattern system was applied to two models of feedback to demonstrate its utility as a lens through which to analyze various instances of feedback and to foreshadow its potential broader applicability as a tool to facilitate knowledge synthesis in the feedback problem space.
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 | 0.078 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.003 | 0.011 |
| 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".