Empowering our First Nations workforce: evaluation of a First Nations COVID-19 vaccination training program
Bibliographic record
Abstract
BACKGROUND: A COVID-19 vaccination training program was designed for Aboriginal and Torres Strait Islander (First Nations) health workers and practitioners in Queensland to expand their scope of practice to include COVID-19 immunisation. In the setting of a global pandemic, the project aimed to improve vaccination levels and show how First Nations staff are central to community-led responses to effectively address their community's health needs. METHODS: The program, consisting of an online module and face to face workshop, is described and then evaluated with the RE-AIM framework via mixed methods of participant training surveys and qualitative feedback. RESULTS: The program reached 738 online and 329 workshop participants with the majority identifying as First Nations. The 52 workshops were attended by participants from 12 different hospital and health services in Queensland and 13 Aboriginal Community Controlled Health Organisations (ACCHOs). Feedback was positive, with participants rating the training highly. Of the First Nations Health Workers and Practitioners who responded to the workshop follow up survey, the majority (34/40) implemented their new skills in practice helping minimise the impact of COVID-19 outbreaks in their community. Most respondents (38/40) considered vaccination should be permanently in their scope of practice. CONCLUSIONS: The successful implementation of the vaccination training project was an example of First Nations led health care. Improving scope of practice for First Nations health staff can improve not just career retention and progression but also the delivery of primary care to a community that continues to bear the inequity of poorer health outcomes.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".