Training healthcare workers and untrained interpreters in remote collaboration amidst COVID-19
Bibliographic record
Abstract
In the context of the public health emergency response to the COVID-19 pandemic in Quebec in 2020, remote public service interpreting has become, within a few days, an essential practice for maintaining services to migrants and allophone refugees, a particularly vulnerable population. This study aimed to measure the impact of two training courses on remote collaboration for mediated consultations developed for healthcare workers and untrained interpreters. A total of 79 healthcare workers and 65 untrained interpreters from the province of Quebec were recruited. They completed the trainings, offered as webinars, and answered the two scales (knowledge and self-efficacy) of the Questionnaire de connaissances sur l'interprétation de service publique à distance [Remote Public Service Interpreting Knowledge Questionnaire]. The study employed paired t -tests to assess the effectiveness of both webinars. Findings reveal a positive impact immediately after completion and at a three-month follow-up. However, there was no significant enhancement in interpreters' self-efficacy over the medium term. Given their modality (remote) and duration (30 min for healthcare workers and three hours for interpreters), the training courses are both effective and practical to implement. This study innovatively promotes interprofessional collaboration in public service interpreting and explores online training's potential to enhance both individual and collective efficacy in the field. • Public service interpreting (PSI) has been rising in healthcare • Training in PSI is limited, posing risks to healthcare quality • Remote PSI (RPSI) becomes crucial during COVID-19 • RPSI collaboration training courses positively impact knowledge and self-efficacy
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".