A typology of healthcare interpreter positionings
Bibliographic record
Abstract
Abstract The conceptual vagueness surrounding the role of the public service interpreter can hinder collaboration between interpreters and providers. Inspired by Mason’s work on interpreter positioning, the study aimed to clarify providers’ expectations of the role of the interpreter in order to strengthen interprofessional collaboration. A typological analysis was conducted based on 23 healthcare providers’ perceptions of the public service interpreter. The Typology of Healthcare Interpreter Positionings features eight positionings that reinforce the interprofessional collaboration and nine that compromise it. The result is a concrete portrait that depicts the neutrality of the public service interpreter as a powerful driver of collaboration. Providers also agree that interpreters must be able to take their place in the consultation, even if it means being assertive and modifying the discourse of the interlocutors. Navigating such situations while maintaining the provider’s trust requires interactional knowledge (soft skills), which complements technical knowledge (hard skills). The Typology represents a communication tool that both providers and interpreters can use to foster collaboration. It also represents a professionalization tool as it reiterates the importance of neutrality in the work of public service interpreters and their social positioning as professionals.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".