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Paper

As shown in Figure 2, directed acyclic graphs encoded a priori knowledge for estimating the total effect of rural residence on hesitancy.

We constructed three factor-derived scales capturing confidence, complacency and convenience.

Rural and urban communities diverged sharply in their acceptance of the HPV vaccine.

These cultural dynamics drive vaccine refusal across the rural United States.

Figure 2. Adjusted associations between residence and vaccine hesitancy.

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Overall Assessment

A timely, well-powered study with a clear rural–urban framing. Three things to strengthen before submission: make the causal-adjustment strategy in §3.2 explicit, report reliability for the hesitancy scales and reconcile the subgroup counts in Table 4 with the totals in §4.1. Tightening the causal language in the Discussion will help it read as carefully as it was designed.

Findings

7

Review status unavailable.

Major
Report reliability for the three hesitancy scales

Section 3.1 anchors your conclusions on these scales. Add Cronbach's alpha (or McDonald's omega) and the factor loadings so readers can judge their psychometric security.

Discuss 1
P

PerfectPaper

If alpha is below ~0.7 for the convenience scale, consider reporting omega instead and noting the item that drags it down.

Major
Causal adjustment is underspecified in §3.2

State each backdoor path you are closing and cite the assumption behind every included covariate — reviewers will want the minimally-sufficient adjustment set made explicit.

Discuss 2
P

PerfectPaper

The DAG in Figure 2 implies you're conditioning on a collider (clinic access). Closing that path may bias the estimate — worth a sentence on why it's included.

R. Mehta (co-author)

Agreed. I can pull the original adjustment set from our preregistration.

Major
Subgroup counts in Table 4 don't reconcile with §4.1

The Hispanic young-adult subgroup counts in Table 4 don't add up to the totals reported in Section 4.1. Reconcile the figures or state the exclusions that explain the gap.

Discuss
Major
Soften unsupported causal language in the Discussion

Your design supports association, not population-level causation. Hedge "drive" to "are associated with" or justify the causal claim with the identification strategy from §3.2.

Discuss
Minor
Define "rural" and "urban" operationally

The rural–urban contrast is central to the paper but never defined. State the classification (e.g. RUCA codes) and the threshold you used.

Discuss
Minor
Account for excluded responses with a participant-flow note

About 12% of responses were dropped without a stated rule. Add a short CONSORT-style flow describing who was excluded and why, so the analytic sample is reproducible.

Discuss
Suggestion
Align Figure 2 axis labels with the §2.4 notation

Figure 2 uses β where the text uses θ for the same quantity. Use one symbol throughout so the figure and the model section agree.

Discuss