Bibliographic record
Abstract
PURPOSE: It is increasingly common for health care professionals from developed countries to travel to developing regions of the world to learn or teach. This project aimed to describe the perceptions held by health care professionals in a developing region toward those who visit their communities to learn or teach. METHOD: Semistructured interviews were conducted in July, 2011, with nine health care professionals from the University of Namibia School of Medicine. Questions revolved around participants' perceptions of benefits, harms, and ethical impressions of a health care professional visiting from a developed country. Interviews were tape-recorded, transcribed, and analyzed qualitatively using an inductive, iterative approach. RESULTS: The interview analysis identified three main narratives that shaped participant perceptions of visits: (1) culture, context, and concern, (2) expectations, intentions, and miscommunications, and (3) partnership and the desire to share and gain knowledge. CONCLUSIONS: Participants' comments supported actively seeking out information regarding cultural and environmental context before visiting, completing a needs assessment to ensure that activities are needed and relevant, attempting to formulate long-term sustainable relationships, and traveling with the appropriate attitude. These themes provide valuable insight into how international educational collaborations can be created in order to be mutually beneficial.
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 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".