Integration of interpreters in mental health interventions with children and adolescents: The need for a framework
Bibliographic record
Abstract
Few empirical studies have detailed the specificities of working with interpreters in mental healthcare for children. The integration of interpreters in clinical teams in child mental healthcare was explored in two clinics, in Montreal and Paris. Four focus groups were conducted with interpreters and clinicians. Participants described the development of the working alliance between interpreters and clinicians, the delineation of interpreters' roles, and the effects of translation on the people in the interaction. Integrating interpreters in a clinical team is a slow process in which clinicians and interpreters need to reflect upon a common framework. An effective framework favours trust, mutual understanding, and valorization of the contribution of each to the therapeutic task. The interpreter's presence and activities seem to have some therapeutic value.
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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 | 0.084 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.019 | 0.063 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".