Who knows more about immunization?: Survey of public health nurses and physicians.
Bibliographic record
Abstract
OBJECTIVE: To report the findings of a knowledge survey of nurse and physician immunization providers. DESIGN: Cross-sectional postal survey assessing demographic characteristics and vaccine knowledge. SETTING: British Columbia (BC). PARTICIPANTS: Nurse and physician immunization providers in BC. MAIN OUTCOME MEASURES: Knowledge of vaccine-preventable diseases, vaccines in general, and vaccine administration and handling practices. RESULTS: Survey responses were received from 256 nurses and 292 physicians (response rates of 48.6% and 18.3%, respectively). Most nurses (98.4%) reported receiving immunization training outside of the academic setting compared with 55.6% of physicians. Overall, nurse immunizers scored significantly higher than physician immunizers on all 3 domains of immunization knowledge (83.7% vs 72.8%, respectively; P < .001). Physicians scored highest on the vaccine-preventable disease domain and least well on the general vaccine domain. Nurses with more experience as health care providers scored higher. Physicians scored higher if they were female, served patient populations predominantly younger than 5 years, or received immunization training outside of academic settings. CONCLUSION: In BC, nurse immunizers appear to have higher overall immunization knowledge than physicians and are more likely to receive immunization training when in practice. Physician immunizers might benefit most from further training on vaccines and vaccine administration and handling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".