Designing accountability measures for health professionals: results from a community-based micro-credential: case study on Indigenous cultural safety
Bibliographic record
Abstract
Abstract Background: There is a widespread commitment to implementing anti-Indigenous racism with health organizations in Canada by introducing cultural safety staff training. In partnership with a public health unit in Ontario, Canada, we developed an evaluation tool to assess the performance of staff who completed an online Indigenous cultural safety education course. Aims: To develop an accountability checklist that could be used for annual employee performance reviews to assess the use and level of knowledge received in professional cultural safety training. Intervention: We co-created a professional development accountability checklist. Five areas of interest were identified: terminology, knowledge, awareness, skills, and behaviours. The checklist comprises of 37 indicators linked to our community collaborators’ intended goals as defined in our partnership agreement. Outcomes: The Indigenous Cultural Safety Evaluation Checklist (ICSEC) was shared with public health managers to use during regularly scheduled staff performance evaluations. The public health managers provided feedback on the design, checklist items, and useability of the ICSEC. The pilot of the checklist is in the preliminary stage and data is unavailable about effectiveness. Implications: Accountability tools are important to sustain the long-term effects of cultural safety education and prioritize the wellbeing of Indigenous communities. Our experience can provide guidance to health professionals in creating and measuring the efficacy of Indigenous cultural safety education to foster an anti-racist work culture as well as improved health outcomes among Indigenous communities.
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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 | 0.093 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".