Reducing barriers to Covid-19 vaccines among structurally disadvantaged populations: the role of public health nurses in promoting health equity
Bibliographic record
Abstract
In Canada, the Covid-19 pandemic has exacerbated existing social inequities and has further contributed to the morbidity and mortality of individuals from lower socio-economic backgrounds. Access to evidence-based services, such as Covid-19 vaccinations, is paramount to population health promotion and health equity advocacy. Health equity can be achieved through the promotion of equitable access to healthcare services and equal distribution of resources. Addressing health inequities during the Covid-19 pandemic is a major goal and moral imperative in the public health sector. The purpose of this qualitative research study was to explore the practice roles of public health nurses (PHNs) during the pandemic in regard to their contribution to reducing barriers to Covid-19 vaccines and promoting health equity for structurally disadvantaged clients. A greater understanding of the PHNs’ role in promoting health equity during the pandemic can contribute to excellence in nursing practice, improve client outcomes, and increase awareness about the importance of social justice and health equity action. Interpretive description by Dr. Sally Thorne was chosen as the methodology for this study to explore and understand the roles of PHNs delivering Covid-19 vaccines through the Focused Immunization Team (FIT). The WRHA Public Health Nursing Professional Practice Model by Dr. Cheryl Cusack was used in scaffolding this study. The literature search was completed using PubMed, CINAHL, Google Scholar, and Scopus databases. The articles used in this study were published in English from 2012 to 2024. The literature reviewed included peer-reviewed journal articles, books, as well as grey literature obtained from professional organizations’ websites. A conventional content analysis was used to analyze the data collected through semi-structured interviews with 10 FIT PHNs. During this process, 11 major themes were identified and categorized into five sections. Although the literature on promoting health equity during the pandemic by prioritizing Covid-19 vaccines for vulnerable populations is abundant, there is no sufficient evidence regarding PHNs’ role on the outreach FIT team in the local context of Manitoba. These study results have implications for future PHN practice, research, education, and administrative domains. It is necessary to further explore how local public health outreach programs can optimize equitable service delivery and improve health outcomes for vulnerable clients during global public health outbreaks.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".