Factors Influencing Nurse Practitioners’ Influenza Vaccine Recommendations: An Explanatory Sequential Study
Bibliographic record
Abstract
Background. Nurse practitioners (NPs) represent a population of primary care providers who routinely recommend and administer influenza vaccines. However, scholarly literature defining NPs’ influenza vaccine recommendation practices was found to be lacking, and little is known about the nature of NPs’ influenza vaccine communications with patients or provider-related characteristics that may affect their recommendations. Purpose. The purpose of this research study was to define and characterize the influenza vaccine recommendation practices of primary care NPs in Ontario and to identify provider-specific factors that affect the quality of NPs’ influenza vaccine recommendations. Methodology. An explanatory sequential mixed-methods study was undertaken. Sample and Setting. The quantitative-phase sample consisted of 92 primary care NPs working in the province of Ontario, Canada. Thirteen NPs who completed the survey participated in follow-up interviews. Results. A majority of survey participants (68.5%, n = 63) reported that they “always” recommend the influenza vaccine during influenza season, and half (50.0%, n = 46) reported that they “strongly” recommend influenza vaccination. Nearly four out of five NPs (79.3%, n = 73) use a conversational approach to recommendation. Three quarters of participants (75.0%, n = 69) recommend urgent, same-day influenza vaccination. When patients refuse an initial influenza vaccine recommendation, most NPs (65.2%, n = 60) indicated that they soften their recommendation, and nearly half of NPs (46.7%, n = 43) reported that they are “somewhat likely” to recommend the influenza vaccine again at future appointments with those patients. In multivariate analysis, NPs who had higher personal vaccine uptake, demonstrated increased confidence in their recommendations, had more clinical experience, managed higher patient loads, and practiced in rural settings were more likely to employ high-quality influenza vaccine recommendation strategies. Analysis of participant interviews further indicated that the sum of NPs’ experiences with influenza/influenza vaccination was the most important factor influencing their vaccine recommendations. Conclusion. The findings can help healthcare leaders and NP educators support NPs in delivering high-quality influenza vaccine recommendations. For example, healthcare leaders should strongly promote influenza vaccine uptake among NPs and advocate for timely access to vaccines each fall. NPs may also benefit from vaccination communication training during their initial NP education and through ongoing professional development activities. Through these means, NPs can strive for high influenza vaccination uptake rates and promote the health of their patient populations throughout future influenza seasons.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".