One child, one appointment: how institutional discourses organize the work of parents and nurses in the provision of childhood vaccination for First Nations children
Bibliographic record
Abstract
To effectively support childhood vaccine programs for First Nations Peoples, Canada's largest population of Indigenous Peoples, it is essential to understand the context, processes, and structures organizing vaccine access and uptake. Rather than assuming that solutions lie in compliance with current regulations, our aim was to identify opportunities for innovation by exploring the work that nurses and parents must do to have children vaccinated. In partnership with a large First Nations community, we used an institutional ethnography approach that included observing vaccination clinic appointments, interviewing individuals involved in childhood vaccinations, and reviewing documented vaccination processes and regulations (texts). We found that the 'work' nurses engage in to deliver childhood vaccines is highly regulated by standardized texts that prioritize discourses of safety and efficiency. Within the setting of nursing practice in a First Nations community, these regulations do not always support the best interests of families. Nurses and parents are caught between the desire to vaccinate multiple children and the requirement to follow institutionally authorized processes. The success of the vaccination program, when measured solely by the number of children who follow the vaccine schedule, does not take into consideration the challenges nurses encounter in the clinic or the work parents do to get their children vaccinated. Exploring new ways of approaching the processes could lead to increased vaccination uptake and satisfaction for parents and nurses.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".