| name | arpsych-transparency-and-reproducibility |
| description | Use when documenting the literature search and making any embedded meta-analysis reproducible for an Annual Review of Psychology (ARPsych) review. Covers search transparency, meta-analytic rigor, and open materials; it does not run the narrative search (arpsych-literature-synthesis) or design exhibits (arpsych-tables-figures). |
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:
- How the literature was found and selected — the search protocol from
arpsych-literature-synthesis, written up so a reader could reproduce the coverage.
- 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
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)
