Transparency & Reproducibility (arpsych-transparency-and-reproducibility)

When to trigger

• The review documents a systematic search and you must report it reproducibly
• The review embeds a meta-analysis or any new quantitative synthesis
• You are deciding what to deposit (search log, coding sheet, effect-size data, code)
• A reader or the Committee should be able to verify how the literature was selected

A review reports no new data — so transparency bites elsewhere

A pure narrative review has no dataset of its own, so the transparency obligation does not look like a primary-paper replication package. It bites on two things:

1. How the literature was found and selected — the search protocol from arpsych-literature-synthesis, written up so a reader could reproduce the coverage.
2. Any quantitative synthesis the review itself contributes — if you compute pooled effects, that is original analysis, and it must be reproducible (检索于 2026-06；以官网为准).

Post-replication-crisis, ARPsych readers expect both, and a review that asserts "the literature shows…" with no documented basis reads as less authoritative.

If the review is narrative (no meta-analysis)

• Report the search: databases, terms, date range, inclusion/exclusion, and the stopping rule — a short, near-PRISMA-style account suffices.
• State selection logic: why these studies and not others (especially when the field is large and you are selective).
• Be explicit about replication status of contested effects (this is part of transparency, not just balance).

If the review embeds a meta-analysis

Then you have run original analysis and must meet quantitative-synthesis standards:

Requirement	What to provide
PRISMA-style flow	search → screening → included, with counts at each step
Coding protocol	how effects were extracted/coded; inter-coder reliability
Effect-size dataset	the extracted effects + moderators, deposited
Analysis code	scripts reproducing the pooled estimates and plots
Heterogeneity + bias	I², moderators, funnel/publication-bias diagnostics
Preregistration (if applicable)	protocol/PROSPERO registration where the synthesis was prospective

Deposit data and code in a public repository (e.g., OSF) and cite the DOI in the review.

Required declarations (检索于 2026-06；以官网为准)

Annual Reviews requires authors to disclose potential sources of bias / conflicts of interest and to state funding; prepare these per the author pages. AI tools are not authors. Re-confirm the exact disclosure format on the live Annual Reviews pages.

Checklist

• Search protocol written up reproducibly (databases, terms, dates, in/out, stopping rule)
• Selection logic stated where coverage is selective
• Replication status of contested effects made explicit
• If meta-analytic: PRISMA-style flow with counts
• If meta-analytic: coding protocol + inter-coder reliability reported
• If meta-analytic: effect-size data + analysis code deposited (OSF DOI cited)
• If meta-analytic: heterogeneity and publication-bias diagnostics reported
• COI / potential-bias disclosure + funding prepared; AI not listed as author

Anti-patterns

• "The literature shows…" with no documented search behind the claim
• Reporting pooled effects with no deposited data or code (irreproducible meta-analysis)
• A meta-analysis with no heterogeneity or publication-bias assessment
• Treating a review's transparency like a primary-paper replication package (wrong object)
• Omitting the conflict-of-interest / potential-bias disclosure Annual Reviews requires
• Listing an AI tool as an author or hiding its use where disclosure is required

Output format

【Review type】narrative | embedded-meta-analysis
【Search transparency】protocol documented reproducibly? Y/N
【If meta-analysis】PRISMA flow + coding + reliability? Y/N
【Open materials】effect data + code deposited (OSF DOI)? Y/N | N/A
【Heterogeneity / bias】I² + funnel/pub-bias reported? Y/N | N/A
【Declarations】COI / bias disclosure + funding prepared; AI not author? Y/N
【Next step】→ arpsych-editor-strategy (align scope/timeline with the Editor)