Evaluation of the Aboriginal Relationship and Cultural Competency Courses among a sample of Indigenous Services Canada nurses
Bibliographic record
Abstract
In 2015, Cancer Care Ontario launched the Aboriginal Relationship and Cultural Competency (ARCC) courses, which stress the importance for healthcare professionals to understand and apply First Nations, Inuit, and Métis (FNIM) cultural safety to provide effective person-centred care. The courses address a key recommendation from the Truth and Reconciliation Commission of Canada report, to provide skills-based training in cultural competency, conflict resolution, human rights and anti-racism. The objective of the evaluation is to validate the tool to assess: if the delivery mechanism is appropriate and feasible; if participants acquire an increased knowledge of the courses' contents; and if positive change in how healthcare practice is delivered is perceived to have resulted. After the ARCC courses were mandated for Indigenous Services Canada (ISC) nurses, an anonymous survey was delivered and a focus group was conducted at a regional meeting. The responses from the surveys were gathered in an excel spreadsheet for analyses and the focus group data were analyzed for key themes. All the nurses in attendance completed the survey (n=22) and a portion participated in the focus group (n=8). Our evaluation demonstrated that free, online, module formatted courses were appropriate and relevant for ISC nurses (81%); the courses increased the knowledge about FNIM people (72%); and the nurses have/will apply what they learned in their practice (82%). There has been an increasing movement for regions and organizations in Canada to complete cultural competency training. Our evaluation demonstrated that free, online, module formatted courses were successful at meeting learning objectives.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".