Sept stratégies pour collaborer avec l’interprète de service public
Bibliographic record
Abstract
Introduction Public Service Interpreting (PSI) is gradually becoming part of Quebec society. The institutional and organizational mechanisms designed to provide a framework for this interpretative practice are not yet generalized to all public services in the province, and where they are, many challenges remain. Objective The objective of the study was to provide stakeholders working in the mental health sector with practical strategies to foster collaboration with public service interpreters. Methodology A critical reading of the literature (narrative review) was carried out with the objective of offering a new perspective on the already existing object of study. Because it offers a critical synthesis of key information in the field, this method is particularly well suited to the needs of the reader who is not up to date with advances in the field. Results This critical reading first made it possible to identify two general challenges concerning mental health practitioners called upon to collaborate with public service interpreters: the recognition of the interpreter's role and the acknowledgement of the diversity of communicative situations likely to foster their collaboration. This paper presents seven interprofessional collaboration strategies that mental health practitioners can apply to meet these challenges: 1) using vocabulary drawn from the public service interpreter Positioning and Postures Typologies; 2) obtaining information to be transmitted in the interpreting assignment; 3) allocating time for briefings and 4) for debriefings; 5) encouraging the interpreter's presentation; 6) framing small and big talk; and 7) promoting the continuity of care. Discussion The present study offers a new perspective on some of the distinctive tensions in the PSI domain. The 7 proposed collaboration strategies are a response to these and aim to improve the quality of intercultural communication and services offered to users by promoting knowledge transfer. They are specifically addressed to knowledge users, take their practices into account and are explained using non-specific terms.
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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 | 0.068 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.029 | 0.021 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| 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".