CASE 7: Implementation Research: A Strategy for Developing Indigenous-Specific Intercultural Competency Training Programs (Part B)
Bibliographic record
Abstract
Nia Singh is an intercultural education specialist and leads the Intercultural Safety Training Program (ISTP) at the Southwestern Ontario Intercultural Education Centre (SOIEC). She has undertaken significant work with the ISTP developing and implementing training sessions and webinars to help clients make their workplace more culturally competent. Nia has recently observed that the ISTP could greatly benefit from including training materials to help health care practitioners provide improved health care to Indigenous patients. Indigenous people face numerous social, political, historical barriers while accessing healthcare services in Canada. Cultural differences can also lead healthcare practitioners to discriminate against their Indigenous patients and consequently, lead to worsening health outcomes (Harfield et al., 2018). Therefore, seeing the need for an Indigenous-specific program aimed at improving the intercultural competence and awareness of health care professionals, Nia contacted relevant stakeholders to help her research and develop a training module. With the background research and stakeholder input complete, Nia is finalizing the training module and delivery plan. She is now faced with the task of optimally implementing the training and assessing the challenges that may arise as the training is disseminated to its intended audience. During the implementation phase of the process, Nia collaborates with prospective clients to ensure the training module is used effectively and successfully fosters important dialogue about health equity and patientcentred care among health care professionals. At the end of the case, Nia decides to collaborate with the Middlesex-London Public Health Unit’s Indigenous Health Coordinator, Vanessa Anderson, to draft an implementation research proposal so she can assess the impact of the new training program and evaluate it as it is disseminated in a practical, real-world setting.\nThis case is intended to provide students practice with contextualizing an implementation research plan so they can assess an Indigenous-specific cultural safety training program through an Indigenous lens. In addition, it will help students consider the value of multiple stakeholder perspectives while implementing these types of programs.
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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 | 0.100 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.014 | 0.015 |
| 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".