Understanding and Embracing Culture in International Faculty Development
Bibliographic record
Abstract
Introduction: Research on international faculty development programs (IFDPs) has demonstrated many positive outcomes; however, participants' cultural backgrounds, beliefs, and behaviors have often been overlooked in these investigations. The goal of this study was to explore the influences of culture on teaching and learning in an IFDP. Method: Using interpretive description as the qualitative methodology, the authors conducted semi-structured interviews with 15 Fellows and 5 Faculty of a US-based IFDP. The authors iteratively performed a constant comparative analysis to identify similar patterns and themes. Transformative Learning Theory informed the analysis and interpretation of the results. Results: This research identified three themes related to the influences of culture on teaching and learning. First, cultural differences were not seen as a barrier to learning; instead, they tended to act as a bridge to cultural awareness and network building. Second, some cultural differences produced a sense of unease and uncertainty, which led to adaptations, modifications, or mediation. Third, context mattered, as participants' perspectives were also influenced by the program culture and their professional backgrounds and experiences. Discussion: The cultural diversity of health professions educators in an IFDP did not impede learning. A commitment to future action, together with the ability to reflect critically and engage in dialectical discourse, enabled participants to find constructive solutions to subtle challenges. Implications for faculty development included the value of enhanced cultural awareness and respect, explicit communication about norms and expectations, and building on shared professional goals and experiences.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".