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Record W4368340874 · doi:10.1111/medu.15118

Meaning making about performance: A comparison of two specialty feedback cultures

2023· article· en· W4368340874 on OpenAlexaff
Margaret Bearman, Rola Ajjawi, Damian J. Castanelli, Charlotte Denniston, Elizabeth Molloy, Natalie Ward, Chris Watling

Bibliographic record

VenueMedical Education · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
FundersDeakin UniversityRoyal Australasian College of Surgeons
KeywordsSpecialtyMeaning (existential)Medical educationPsychologyGrounded theoryQualitative researchConstructivist grounded theoryMedicineNursingFamily medicineSociologyPsychotherapist

Abstract

fetched live from OpenAlex

INTRODUCTION: Specialty trainees often struggle to understand how well they are performing, and feedback is commonly seen as a solution to this problem. However, medical education tends to approach feedback as acontextual rather than located in a specialty-specific cultural world. This study therefore compares how specialty trainees in surgery and intensive care medicine (ICM) make meaning about the quality of their performance and the role of feedback conversations in this process. METHODS: We conducted a qualitative interview study in the constructivist grounded theory tradition. We interviewed 17 trainees from across Australia in 2020, eight from ICM and nine from surgery, and iterated between data collection and analytic discussions. We employed open, focused, axial and theoretical coding. FINDINGS: There were significant divergences between specialties. Surgical trainees had more opportunity to work directly with supervisors, and there was a strong link between patient outcome and quality of care, with a focus on performance information about operative skills. ICM was a highly uncertain practice environment, where patient outcome could not be relied upon as a source of performance information; valued performance information was diffuse and included tacit emotional support. These different 'specialty feedback cultures' strongly influenced how trainees orchestrated opportunities for feedback, made meaning of their performance in their day-to-day patient care tasks and 'patched together' experiences and inputs into an evolving sense of overall progress. DISCUSSION: We identified two types of meaning-making about performance: first, trainees' understanding of an immediate performance in a patient-care task and, second, a 'patched together' sense of overall progress from incomplete performance information. This study suggests approaches to feedback should attend to both, but also take account of the cultural worlds of specialty practice, with their attendant complexities. In particular, feedback conversations could better acknowledge the variable quality of performance information and specialty specific levels of uncertainty.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.028
GPT teacher head0.428
Teacher spread0.400 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2023
Admission routes1
Has abstractyes

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