«I Do Not Have Time»—Is This the End of Peer Review in Public Health Sciences?
Open accessAbstract
In a 2019 workshop of the Swiss School of Public Health (SSPH+) -the owner of the IJPH and PHR -all participants agreed that our journals should continue with thorough prepublication peer review, including revisions, point-by-point responses and final decisions made by science editors, to strengthen the quality of publications. But soon after, the explosion in Covid-19 research caused an unprecedented increase in submissions and thus demand for peer review in health science journals (1)(2)(3). During the first 6 months of the pandemic, total publications and Covid-19 related publications increased exponentially (2). Submissions to JAMA almost tripled (4) and to IJPH more than tripled. Elsevier's health and medical journals saw a 92% increase in submissions between February and May 2020 (3). The publication rates of all peer reviewed public-health related articles increased by 25% from 2019 to 2020, and by 21% from 2020 to 2021, exceeding the annual publication growth rates before the pandemic which were 4, 15, 9 and 19% from 2016 until 2019 (Web of Science, category Public, Environmental and Occupational Health).The growth in publications has brought the peer review system to the edge, as the commitment of scientists did not keep pace with the increase in manuscripts. For Elsevier's health and medical journals a 50% decreasing proportion of peer review invitation 37 acceptances have been disclosed for February to May 2020 compared to the same period in 38 2019 (3). The records of IJPH provide sobering facts, too. "I do not have time" has become a 39 leading response -and even worse: the majority does not respond at all. In 2021, 53% of 40 (repeated) invitations for peer review remained un-answered, whereas 38% actively 41 declined. Both, in 2021 and 2022, only 9% of all invited reviewers agreed to provide a 42 review. In contrast, during the years prior to the pandemic, we observed a rather stable rate 43 of 35-40% who would agree and deliver a review report.The consequences of this crisis are equally bad for our authors and editors. First, the 46 endless search for reviewers has slowed down the publishing process substantially. Second, 47 editors are forced to release automatic "mass-invitations" to 20+ potential reviewers at a time 48 and to remind those repeatedly, thus, flooding the stressed community with even more 49 invitation emails. Third, automated search strategies require artificial big-data search 50 engines. As those are of limited intelligence yet, a rather high rate of 25% -probably the tip 51 of an iceberg -of active decliners tell us "this is not my field". Fourth, we are forced to 52 discuss whether and under what conditions final decisions should be based on the 53 feedbacks of only one review. And last but not least, the workload for our handling editors 54 has also increased substantially, which in turn triggers their resistance to handle 55 manuscripts.The simple truth is: if we collectively "do not have time" to review manuscripts, there will be 58 no pre-publication peer review anymore. Thus, key questions emerge. Does public health 59 science serve authorities and the public sufficiently if research is published without formal 60 pre-publication peer review? Is post-publication peer review also appropriate to promote 61 good public health sciences? Will publishing on pre-print servers become the new standard 62given the abundance of innovation in this field (5). In the absence of clear evidence for the 63 opposite, we strongly believe that the multi-disciplinary public health sciences profit from pre-64 publication peer review. Thus, we need a solution to address the crisis.Given the ubiquity and magnitude of the problem, we call for concerted strategies also of 66 publishers to resolve it together with journal editors as they cannot do it alone. The literature 67 discusses various incentives for reviewers, among them discounts on publisher's products, 68 certificates, recognizing the best reviewers, increasing diversity in the peer-review process 69 and financial incentives (5-8). The development of reviewer recognition platforms (ORCID, 70Clarivate 's reviewer recognition platform) is a positive example.From a quantitative perspective, the solution looks rather straightforward in our typically 73 multi-authored science: if researchers agree to review as many papers per year as they write 74 as first or last author, the crisis would resolve. Indeed, the trends in scientific publishing 75where the number of publications double every ten years whilst the number of scientists 76 increases by only 21% ( 7) calls for such level of commitment. When reviewers are asked, a 77 vast majority (85%) find recognition and training will improve the efficacy of peer review and, that universities and employers should explicitly require and recognize the reviewing work 79 which should be career enhancing (9). 80 81Our two journals have adopted a feature to remove at least barriers of the peer revie
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing peer review processes do not scale well given the rapid growth of AI research communities and of science as a whole.
“Given the rapid growth experienced by AI research communities in particular (Künzli et al., 2022), and by science as a whole (Landhuis, 2016), existing processes do not scale well.”
From What Can Natural Language Processing Do for Peer Review? (2024) · cites this paper as Künzli et al., 2022
The paper’s main text (page unavailable) says publication growth has brought peer review to the edge because reviewer participation has not kept pace. It documents growth in public-health and COVID-19 research, but not in AI research communities—the specific point to which the citing sentence attaches Künzli et al. (LLM, conf=0.84)
Experimental — accuracy not yet measuredReviews suffer from poor quality and high variance.
“Reviewers have less time to complete reviews; consequently, reviews suffer from poor quality and high variance (Rogers and Augenstein, 2020; Künzli et al., 2022).”
From The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors (2025) · cites this paper as Künzli et al., 2022
In the paper’s main text (page unavailable), the authors report that “I do not have time” is a leading reason for declining review invitations and describe a resulting shortage of reviewers. They do not say that completed reviews are of poor quality or highly variable, or establish that reduced time causes those outcomes. (LLM, conf=0.81)
Experimental — accuracy not yet measuredReviewers have less time to complete reviews.
“Reviewers have less time to complete reviews; consequently, reviews suffer from poor quality and high variance (Rogers and Augenstein, 2020; Künzli et al., 2022).”
From The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors (2025) · cites this paper as Künzli et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
How a torrent of COVID science changed research publishing — in seven charts
Else, H. (2020) · Nature · doi:10.1038/d41586-020-03564-y · Cited by 387 · see citing works
Aviv-Reuven, S., & Rosenfeld, A. (2021) · Scientometrics · doi:10.1007/s11192-021-04059-x · Cited by 205 · see citing works
Editorial Evaluation and Peer Review During a Pandemic
Bauchner, H., Fontanarosa, P., & Golub, R. M. (2020) · JAMA · doi:10.1001/jama.2020.11764 · Cited by 99 · see citing works
Publons' Global State Of Peer Review 2018
Publons (2018) · doi:10.14322/publons.gspr2018 · Cited by 79 · see citing works
Ellwanger, J. H., & Chies, J. A. B. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.02.001 · Cited by 46 · see citing works
Peer-reviewing in Surgical Journals: Revolutionize or Perish?
Chloros, G. D., Giannoudis, V. P., & Giannoudis, P. V. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000004756 · Cited by 34 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [3]Squazzoni F Bravo G Grimaldo F García-Costa D Farjam M Mehmani B. Only Second-Class Tickets for Women in the COVID-19 Race. A Study on Manuscript Submissions and Reviews in 2329 Elsevier Journals. PLoS ONE (2020) 16(10): e0257919. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3712813 (16 September 2022). 10.1371/journal.pone.0257919 PMC8528305 34669713 · DOI
- [5]Kaltenbrunner W Pinfield S Waltman L Woods HB Brumberg J. Innovating Peer Review, Reconfiguring Scholarly Communication: An Analytical Overview of Ongoing Peer Review Innovation Activities. SocArXiv (2022). [Internet]Available from: osf.io/preprints/socarxiv/8hdxu (16 September 2022)
- [7]Brainard J. The $450 Question: Should Journals Pay Peer Reviewers? In: Payment Advocates Expect Quicker, Better Reviews but Opponents Fear Unsustainable Costs. 1 March 2021. Science news/Scienceinsider/ Scientific Community Available from: https://www.science.org/content/article/450-question-should-journals-pay-peer-reviewers (16 September 2022).
Cited by
Sorted by citations
What Can Natural Language Processing Do for Peer Review?
Kuznetsov, I., Afzal, O. M., Dercksen, K., Dycke, N., Goldberg, A., Hope, T., Hovy, D., Kummerfeld, J. K., Lauscher, A., Leyton-Brown, K., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.06563 · Cited by 6 · see citing works
The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors
Sadallah, A., Baumgärtner, T., Gurevych, I., & Briscoe, T. (2025) · arXiv · doi:10.48550/arxiv.2509.04484
“Reviewers have less time to complete reviews; consequently, reviews suffer from poor quality and high variance (Rogers and Au-genstein, 2020; Künzli et al., 2022).”
- Authors
- Künzli, N., Berger, A., Czabanowska, K., Lucas, R., Madarasova Geckova, A., Mantwill, S., & von dem Knesebeck, O.
- Affiliations
- Swiss Tropical and Public Health Institute (CH)
- Maastricht University (NL)
- Institute of Public Health, University of Porto (PT)
- University of Lucerne (CH)
- Corresponding author
- Künzli, Nino
- Venue
- Public Health Reviews
- Publisher
- Frontiers Media SA
- Published
- 2022-11-17
- Volume
- 43
- Pages
- 1605407-1605407
- ISSN
- 0301-0422 (ISSN-L)
- 2107-6952
- PMC
- PMC9716458
- OpenAlex
- W4309457783
- Cited by
- 17
- References
- 6
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 7 · 2023: 2
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Public health reviews (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- edoc (University of Basel) (acceptedVersion)
- Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Publications (Maastricht University) (submittedVersion, cc-by)
- edoc (University of Basel)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Keywords
- peer review, COVID-19 pandemic, reviewer shortage, reviewer recruitment, peer review turnaround time, public health journals, open peer review, scholarly communication reform, Elsevier, Journal of the American Medical Association
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review