Effect of Monetary Incentives on Peer Review Acceptance and Completion: A Quasi-Randomized Interventional Trial
Bibliographic record
Abstract
OBJECTIVES: Peer review typically relies on experts volunteering their time to review research. This process presents challenges for journals that may face a shortage of qualified referees, resulting in either delay in handling papers or less thorough review than is optimal. We experimentally tested the impact of providing cash incentives to complete peer review assignments at Critical Care Medicine . DESIGN: Quasi-randomized, blinded, interventional study with an alternating treatment design. SETTING: Critical Care Medicine (CCM ), a peer-reviewed specialty journal. SUBJECTS: All reviewers receiving requests from CCM to review research articles during a 6-month period from September 2023 to March 2024 (excluding a 2-wk holiday window). INTERVENTIONS: In alternating 2-week blocks, reviewer invitation letters were sent out, including either an offer of $250 for accepting the peer review request (treatment) or the standard letter with no cash offer (control). Reviewers who fulfilled incentivized invitations received a $250 check from the journal. MEASUREMENTS AND MAIN RESULTS: Our primary outcome was the rate of invitation-to-completed-review conversion, defined as the number of reviews submitted divided by the number of reviewer invitations sent out. Secondary outcomes included the "on-time" conversion rate, invitation acceptance rate, time to invitation acceptance, time to review submission, and review quality. Seven hundred fifteen reviewer invitations were sent out, 414 of which (57.9%) included an incentive offer. Two hundred eighteen (52.7%) of the incentivized invitations were accepted, compared with 144 (47.8%) in the control group. A greater proportion of reviewer invitations led to submitted peer review reports in the incentive group than in the control group (49.8% [206/414] vs. 42.2% [127/301]; p = 0.04). In a "survival analysis," invitations sent with an incentive offer were fulfilled faster on average (Cox proportional hazard ratio, 1.30 [1.04-1.62]; p = 0.02), corresponding to quicker review times of approximately 1 day (11 vs. 12 d). Of the 333 reviewer reports submitted, 205 (61.6%) were assessed by editors, with no difference in review quality noted between study arms. CONCLUSIONS: Providing cash incentive for completing peer review reports resulted in a modest increase in the share of invited reviewers who complete reviews for a specialty medical journal.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".