CASE 14: Hiring a Competent Health Promoter: Can Competency Statements Help?
Bibliographic record
Abstract
Saraz Frasier has been the manager of Special Programs and Healthy Communities at her health unit for the past five years. She is a true trailblazer within her organization. Saraz has helped transform her team into an innovative, progressive, and health equity-driven team. This team is responsible for promoting health, planning, conducting, and implementing health initiatives and health programs, working with community partners, developing policy, and reducing disparities within the community.\nSaraz has recently been tasked with hiring a new health promoter for her Healthy Communities team. This new hire will help lead Saraz’s team in health promotion and help plan special health programs. She is determined to find a candidate who will understand and contribute to her team’s current dynamic, work ethic, and equity-related priorities, and to the organization’s vision and desired culture change.\nThe top three candidates were already interviewed this past week. Saraz now needs to decide who the best person is for this position. As she is thinking about finding a reliable way to evaluate and compare the three excellent candidates, Saraz opens her email only to find a webinar on the Pan-Canadian Health Promoter Competencies. It’s a sign! She will use the Competencies to evaluate and compare her three candidates in order to hire the best person for the job.\nThe Pan-Canadian Health Promoter Competencies outline the skills, knowledge, and abilities that health promoters should possess to fulfill their mandate efficiently and adequately (Health Promotion Canada, 2015). These Competencies serve as a framework upon which health promoters, and others who work within health promotion, can base their work and practice their skills in targeting health, health equity, and the social determinants of health (Health Promotion Canada, 2015). Saraz can strategically use these competency statements to create a profile of the perfect candidate for the position, based on the skills, abilities, and knowledge that she requires, and then compare the three qualified candidates to this profile. The candidate who best reflects the Health Promoter Competencies and Saraz’s ideal candidate profile must be chosen soon, as these skills are required to undertake the type of work conducted by the exemplar Healthy Communities team at Saraz’s health unit.\nAll three of Saraz’s candidates are competent, skilled, and knowledgeable. Saraz is looking for an innovative leader who possesses the required education and experience, and understands and values the complexities involved in public health. Saraz has to take one last look at her candidates, using the ideal candidate profile she has developed based on the Competencies, to determine who is most likely to best fulfill her expectations of a competent health promoter.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 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".