كتابة تعليق

1. Does the introduction explain the objective of the research presented in the preprint?

Yes.

The introduction establishes the public health importance of HPV vaccination and how high HPV vaccine hesitancy, particularly in sub-Saharan African settings like Cameroon, can make it difficult to get children vaccinated. It identifies a research gap concerning the lack of any locally developed and internally validated predictive models for HPV. The study objective is clearly stated, which is to develop and compare machine learning models, and identify important predictors of parental HPV vaccine hesitancy.

2. Are the methods well-suited for this research?

Yes.

The comparison of logistic regression, random forest, and XGBoost is appropriate for assessing whether more complex algorithms offer predictive advantages over a conventional statistical model. The authors also used cross-validation, discrimination, and SHAP-based interpretation. The authors do acknowledge possible issues with the generalizability of their findings due to how the participants were selected using convenience sampling, and that because all participants were from one area West Africa there are limits to the data’s representativeness. Additionally, the data and analysis code are not readily available in the current preprint; therefore, sharing the analysis code and any model specifications could improve methodological transparency.

3. Are the conclusions supported by the data?

Somewhat.

The results support the conclusion that all three models utilized demonstrate reasonably strong discrimination within the test sample, and that more complex predictive models like the XGBoost did not show a clear advantage over logistic regression. The finding that information sufficiency, perceived vaccine safety, and institutional trust are important predictors of vaccine hesitancy is also consistent across the 3 model approaches. The proposed use of a targeted screening tool in routine immunization settings is a reasonable future direction, but it is only a potential benefit yet to be supported. The main concern is ensuring that they are not attempting to establish a causal relationship.

4. Are the data presentations, including visualizations, well-suited to represent the data?

Yes.

The use visualizations such as ROC and calibration curves, the table of ranked predictors, another table outlining all predictors used, and a model-performance table, all seemed appropriate for this comparative prediction study.

5. How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Clearly.

The discussion is well structured and connects the results to the broader literature on HPV vaccination, public health, and machine learning. The observation that logistic regression performed comparably to more complex algorithms indicates that greater model complexity may not always produce better predictions. The authors also identify relevant next steps, including how further research may consider ways to increase external validation and advocating for immunization services. My main concern is that explanations involving information hesitancy predictors may not be fully established by their cross-sectional analyses.

6. Is the preprint likely to advance academic knowledge?

Yes.

The study addresses an important public health problem in their study location, and contributes locally relevant evidence from Cameroon, a setting in which they have identified predictive modelling of parental HPV vaccine hesitancy to be limited. The main contribution of this preprint may be for hypothesis generation rather than changing practices in the field.

7. Would it benefit from language editing?

No.

8. Would you recommend this preprint to others?

Somewhat yes.

I would recommend this preprint to researchers working on vaccine hesitancy, public health prediction modeling, and the application of machine learning in low- and middle-income countries. This is because the comparison of modeling approaches and the emphasis on local evidence make it a useful contribution for further investigation. However, I would recommend readers interpret the findings as preliminary evidence of predictive performance within this one West African district, rather than as evidence that the models are ready for any large scale implementation. Additionally, these findings should not be used to justify individual-level risk or interventions without further research.

9. Is it ready for attention from an editor, publisher, or broader audience?

Somewhat Yes.

The manuscript addresses a relevant public health issue with a clearly stated objective, and reports a well thought comparison of three predictive modelling approaches. I would prioritize further discussion of the sampling limitations, more cautious interpretation of the patterns found, and possible open access to analysis code.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.