Cross-cultural adaptation and validation of a teaching questionnaire measuring facilitator competencies and characteristics of interprofessional clinical educators in an Asian setting
Bibliographic record
Abstract
This study aimed to validate an Indonesian version of the teaching questionnaire measuring the competencies of interprofessional education (IPE) facilitators and the characteristics of good clinical educators described by Kerry et al. (2021). A cross-cultural adaptation was developed and consisted of the following steps: forward–backward translation, content validity index measurement, cognitive interviews and a pilot study to measure content validity and reliability, exploratory factor analysis (EFA) to identify the new dimensionality, and confirmatory factor analysis (CFA) to confirm the measurement model. The pilot study results confirmed that the Indonesian version of the questionnaire assessing teaching competencies had good internal consistency (ω= .74 for the competencies of facilitators and ω= .88 for the characteristics of good clinical educators). The questionnaire was then administered to 209 clinical educators from five health professions. The EFA revealed two factors for the competencies (ω1= .86, ω2 = .70) and one factor for the characteristics of good IPE clinical educators (ω= .90). The CFA showed that the proposed model had a good fit with the observed data with (chi-square test: p > .05; CMIN/df, TLI, CFI, GFI, and AGFI were within the expected ranges; and RMSEA approximately .05)
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".