Intentional Teaming: Experiences from the Second National Healthcare Symposium
Bibliographic record
Abstract
Abstract The second National Symposium on Healthcare Interpreting was developed and hosted by the CATIE Center of St. Catherine University in St. Paul, MN, on July 22-25, 2012. As one of six centers funded by the U.S. Department of Education, Rehabilitation Services Administration to advance interpreter education, the goal of this symposium is “to improve the understanding of the complex role of interpreters, including the linguistic, cultural, social and ethical challenges inherent in these settings” (CATIE Center, n.d., National Symposium on Healthcare Interpreting section, para. 2). In 2012, 135 Deaf and hearing interpreters, interpreter coordinators and health care providers from 25 states, Canada and Australia attended the symposium to access research and best practices in medical and mental health interpreting. Attendees had the opportunity to advance their understanding of the complex nature of healthcare interpreting work, particularly with peers and professionals. Plenary and concurrent sessions were presented in either American Sign Language (ASL) or English, with interpretation provided for nearly all sessions. The symposium audience of healthcare providers and experienced interpreters, combined with dense, technical content and varying interpretation needs, presented unique challenges for the symposium interpreting team. This article takes a closer look at those challenges and the process of intentional teaming that occurred so that the team could successfully provide effective interpreting services during the symposium. It also provides a documentation of the symposium’s successful team approach, processes and reflections. Note that the information in this article is provided with the consent of the interpreting team and the presenters’ gracious permission. Specific names are not used in the article because the focus is on the team and its dynamic rather than specific individuals. In this way, it is hoped that the examples and strategies shared can be applicable in other situations with different teams and events.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.012 |
| 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".