A Standardized Narrative Profile Approach to Self-Reflection and Assessment of Cross-Cultural Communication
Bibliographic record
Abstract
Objectives: 1) to explore clinical assessor’s values regarding behaviours related to cultural aspects of care, 2) to generate standardized narrative profiles regarding cultural behavioural outcomes within clinical teaching settings, and 3) to rank order standardized narrative profiles according to performance expectations. Methods: Ten interviews were completed with clinicians to determine values and performance expectations for culturally competent behaviours. Transcripts were produced and coded. Six narrative profiles were developed based on data obtained. Twenty clinicians categorized profiles according to performance expectations and rank ordered. Intraclass correlation coefficients (ICCs) determined inter-rater reliability. Clinicians rated usability of profiles in clinical training settings. Results: Eighteen categories were coded with communication, awareness and ability most frequently reported with each ranging from 9.6-11.5% of the utterances. Consensus for categorization of all profiles was achieved at a level of 70% (ICC = 0.837, 95% CI 0.654-0.969). High inter-rater reliability was achieved for rank ordering (ICC = 0.815, 95% CI 0.561 to 0.984). Seventeen (85%) clinicians agreed that the profiles would be usable in clinical training settings. Conclusions: Standardized narrative profiles may aid assessment and self-reflection for student performance within culturally diverse interactions. Conflict of Interest We declare no conflicts of interest or financial interests that the authors or members of their immediate families have in any product or service discussed in the manuscript, including grants (pending or received), employment, gifts, stock holdings or options, honoraria, consultancies, expert testimony, patents and royalties. Type: Original Research
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".