33 What role can internationally educated health professionals play in shaping vaccine attitudes in their communities?
Bibliographic record
Abstract
Background Vaccine hesitancy has been mainly studied in the context of patient care with little attention paid to the health care providers’ attitudes towards vaccinations. COVID-19 pandemic demonstrated the importance of understanding the role health care professionals play in promoting vaccines. Internationally educated health care professionals (IEHPs) practicing in primary care can shape patients’ attitudes towards vaccinations, but we know next to nothing about their views on vaccines and the role they can play in promoting vaccinations in their communities. Aim We explore how the intersection of professional, gender, and ethnic/racial identities of IEHPs practicing in Canada shape their views on vaccinations. We examine how do IEHPs’ views on vaccines are shaped by the intersection of their professional and personal identities; what concerns they may have when communicating vaccine-related information to their patients; and how their intersecting identities can shape provider-patient communication. Methods We conducted 20 open-ended interviews with IEHPs practicing in Canada as physicians, nurses, dentists or pharmacists. The interviews were coded in NVIVO 12 using inductive analysis and applying an intersectional lens. Results The vast majority of IEHPs had positive attitudes towards vaccines and had a strong sense of trust in public health officials. Some participants also saw access to vaccines as a privilege of practice in the global north and vaccine hesitancy as a ‘first-world problem’. The participants pointed out, however, that employment precarity combined with gender/racial/ethnic and professional identity may shape their ability to freely communicate their opinions about vaccines to their patients. Conclusions Despite the challenges related to employment precarity and workplace discrimination, IEHPs can be effective ambassadors of health-related information in their communities, especially when there is an alignment between IEHPs’ and their patients’ ethnic and cultural identities. It is important to support IEHPs in this work and empower them as agents of change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".