Equity of access: Consent interpretation program at Princess Margaret Cancer Centre (PM) in Canada.
Bibliographic record
Abstract
189 Background: Toronto is a multicultural city with over 160 languages spoken by patients. Since 2010, institutional policy requires that professional medical interpreters are used when obtaining informed consent from patients with limited English proficiency (LEP). The availability and cost of these interpreters can be a deterrent for clinical trial participation, particularly when funding is limited. In order to ensure that patients facing language barriers have equitable access to trials, to protect the rights and safety of LEP patients involved in trials, and to improve patient outcomes, adherence to this policy needs to be ensured. Methods: Through a collaboration with the PM Cancer Clinical Research Unit (CCRU) and Interpretation and Translation Services (ITS) supported by the Princess Margaret Cancer Foundation, a 6-month pilot was initiated with full access to interpretation services for all trial patients in November 2012. The CCRU provided training to interpreters on clinical trials and GCP and interpreters reviewed template consent forms provided by ethics boards to cut back on preparation time and costs when delivering a sight translation of study specific consent forms. Trials staff were trained on the process and given badge tags with instructions. Metrics were collected to monitor the use of professional interpreters. Results: Utilization of professional interpreters in trials increased by 16% during the 6-month pilot and 286 requests have been logged to date. Staff were surveyed and indicate this has streamlined the consent process with 83% of respondents saying the new process is easy/very easy. Care providers feel this has allowed them to approach more patients than before this project. Conclusions: This project ensures that accurate information is provided to all patients contemplating participating in or already enrolled in trials, that all patients have the same level of access to treatment, and that there is equity of access for all patients irrespective of their English proficiency. The increased use of professional interpreters in consent discussions indicates better adherence to policy which has allowed the pilot to continue for another year.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".