Brazilian version of the Calgary-Cambridge Observational Guide (CCOG): cross-cultural adaptation and psychometric properties
Bibliographic record
Abstract
Abstract Introduction The search for appropriate tools to assess communicational skills remains an ongoing challenge. The Calgary-Cambridge Observational Guide is designed for measuring and comparing performance in communication skills training Objective Adapt the 28-item version of the guide for the Brazilian cultural context and perform a psychometric quality analysis of the tool. Method Experienced preceptors (35) evaluated videos of 5 medical residents with a simulated patient. using the translated version. For the cultural adaptation we followed the methodological norms on synthesis, retro-translation, committee review, and testing. We conducted the data analysis with the Rasch Many-Facet Model. Results The reliability of the internal consistency was satisfactory, with the Cronbach's alpha coefficient acceptable in all 5 evaluations (0.88; 0.84; 0.89; 0.87; 0.83). The reliability coefficient was high in the evaluators’ facets (0.90), the stations (0.99), and items (0.98). The data indicates the suitability of the tool to the Rasch Many-Facet Model. The evaluators had greater difficulty with attitudinal items, such as demonstration of respect, confidence, and empathy. Conclusion The psychometric properties of the tool were adequate. The reliability indicators of the Rasch Model presented a good potential for reproducing the Brazilian version of the tool as well as acceptable reliability for its use.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".