Entity linking (EL) is the task of linking the text segments to the referring entities in the knowledge graph, typically decomposed into mention detection, and entity disambiguation. Compared to traditional methods treating the two tasks separately, recent end-to-end entity linking methods exploit the mutual dependency between mentions and entities to achieve better performance. However, existing end-to-end EL methods have problems utilizing the dependency of mentions and entities in the task. To this end, we propose to model the EL task as a hierarchical decision-making process and design a hierarchical reinforcement learning algorithm to solve the problem. We conduct extensive experiments to show that the proposed method achieves state-of-the-art performance in several EL benchmark datasets.

End-to-end Entity Linking with Hierarchical Reinforcement Learning

**Objective** In the evolving era of precision medicine, a
subtyping study attempts to define subgroups of a disease
entity wherein certain individuals with the disease share
clinical characteristics (eg, biomarker profile, clinical
manifestations, and prognosis) distinct from others with the
disease. There is an increase in the number of studies on
disease subtyping in the past 40 years, with a rapid surge in
the last decade, yet there is no standardized reporting
guideline or appraisal checklist for subtyping studies to
promote reporting quality. This new guideline aimed to
improve the accuracy and completeness of reporting of
studies of disease subtyping and to aid readers and peer reviewers in assessing the potential for bias in a subtyping
study.

**Design** Members of the International Parkinson and
Movement Disorder Society Task Force on Parkinson’s
Disease’s Subtypes, having performed a systematic review of
subtyping studies of Parkinson disease and found significant
methodologic shortcomings of published subtyping research,1
followed up with a systematic search of the Enhancing the
Quality and Transparency of Health Research (EQUATOR)
Network database and others to find published guidelines
about subtyping research, with no results.2
In response, the
Task Force convened expert panel meetings to develop a
Standards for Reporting Subtyping Studies (StaRSS)
reporting guideline,1
following the steps introduced in the
guidance for developers of health research reporting
guidelines.3
To do so, members of the expert panel created
new items relevant to the context of subtyping using
Parkinson disease as a model. The items were approved after
multiple revisions in expert panel meetings.

**Results** The reporting guideline comprises 37 items,
including numerous novel topics unique to the concepts and
various aspects of a subtyping study mainly related to the
Methods and Results sections of subtyping studies
(**Table 75**). For the other sections, the Task Force adopted
relevant items from existing general guidelines, namely, the
Standards for Reporting of Diagnostic Accuracy (STARD) and
the Reporting of Observational Studies in Epidemiology
(STROBE), with minor adjustments.

![](https://assets.underline.io/uploads/markdown_image/1/image/c59d9c45468c4257c825f97e5c3fa744.png)

**Conclusions** StaRSS is a consensus guideline that describes
essential elements of the design, outcome assessment,
execution of statistical tests, and reporting of subtype
research findings and could guide authors to properly conduct
a subtyping study. Adoption of the StaRSS checklist by
biomedical journals might improve the quality of reporting
and appraisal of subtyping studies by peer reviewers. To
achieve these goals, future studies are needed to assess the
utility of the StaRSS checklist through panel discussions of
the experts in subtyping studies of various fields of
biomedicine.

**References**
1. Mestre TA, Fereshtehnejad SM, Berg D, et al. Parkinson’s
disease subtypes: critical appraisal and recommendations. J
Parkinsons Dis. 2021;11(2):395-404. doi:10.3233/JPD-
202472
2. Altman DG, Simera I, Hoey J, Moher D, Schulz K.
EQUATOR: reporting guidelines for health research. Lancet.
2008;371(9619):1149-1150. doi:10.1016/S0140-
6736(08)60505-X
3. Moher D, Schulz KF, Simera I, Altman DG. Guidance for
developers of health research reporting guidelines. PLoS Med.
2010;7(2):e1000217. doi:10.1371/journal.pmed.1000217

**Conflict of Interest Disclosures** Seyed-Mohammad
Fereshtehnejad received honoraria from the European Science Foundation. Connie Marras is a consultant for Grey Matter
Technologies and receives financial compensation as a steering
committee member from the Michael J. Fox Foundation and the
Parkinson’s Foundation (USA); she received research grants from
the Michael J. Fox Foundation, Canadian Institutes of Health
Research, International Parkinson and Movement Disorder
Society, and the Weston Brain Institute and as a site investigator
for clinical studies sponsored by Theravance Inc, the Michael
J. Fox Foundation, the Parkinson’s Foundation, and Centogene.
David Moher is an associate director of the Peer Review Congress
Advisory Board but was not involved in the review or decision for this abstract. Tiago Mestre reports speaker honoraria from AbbVie
and the International Parkinson and Movement Disorder Society;
consultancies from CHDI Foundation/management and advisory
board from AbbVie; Biogen, Roche, nQ Medical, Medtronic,
Sunovion, and Valeo Pharma; and research funding from EU Joint
Programme–Neurodegenerative Disease Research, University
of Ottawa Brain and Mind Research Institute, Ontario Research
Fund, Canadian Institutes of Health Research, the Michael J. Fox
Foundation, Parkinson Canada, Parkinson’s Disease Foundation/
Parkinson Study Group, LesLois Foundation, PSI Foundation, and
the Parkinson Research Consortium.

**Acknowledgments** We would like to thank all the members of
the Parkinson’s disease subtyping task force at the International
Movement Disorder Society.

![](https://assets.underline.io/uploads/markdown_image/1/image/a8c6856cfcd25f398a00534832288e74.png)

Development of the Standards for Reporting Subtyping Studies (StaRSS) Reporting Guideline

**Objective** Africa ranks second of the 7 continents in
population and geographical area. Reports of research from
Africa are often labeled as being African in scope, a dubious
claim given the low likelihood of analyzing data
representative of 1.4 billion people. Accurate reporting of the
scope of research studies in Africa avoids mischaracterization
and bias that could have significant implications on health
care resource distribution, research funding, and policy
making. This study characterized the geographical scope of
reports of randomized clinical trials (RCTs) conducted in
Africa, because RCTs are most likely to inform clinical
practice and policy.

**Design** A tailored PubMed search was developed using
Medical Subject Headings and keywords to represent Africa,
African, and RCTs. Citations from January 1, 1968, to
February 25, 2022, were included. Titles and abstracts were
reviewed against established inclusion and exclusion criteria
followed by a full-text review of included citations and data
extraction. Based on the number of countries, studies were
categorized as national (1 country), binational (2 countries),
trinational (3 countries), multinational (≥4 countries), and
continentwide (multinational, inclusive of all 5 subregions:
North, Southern, Central, East, and West).

**Results** A total of 285 RCTs met inclusion criteria, and only 1
was continentwide. Of the 54 African countries, 14 (25%)
were not represented in the published RCTs. The top 3
countries were in East Africa, namely, Kenya (80), Uganda
(79), and Tanzania (56). Studies were reported as African
(201 [71%]), sub-Saharan African (45 [16%]), West African
(16 [6%]), East African (14 [5%]), Southern African (7 [2%]),
and East + Central or West + Central African (2 [<1%]). The
median number of countries included by reported scope was
2 (IQR, 1-3) overall, and similar in African and sub-Saharan
African studies; 3.5 (IQR, 2-4) in East African studies; 1 (IQR,
1-2) in West African studies; and 2 (IQR, 2-2) in Southern
African studies. A total of 124 studies (44%) were national, 66
(23%) were binational, 33 (11%) were trinational, and 62
(22%) were multinational. Of 201 studies reported as African,
91 (45%) were national, 49 (25%) were binational, 21 (10%)
were trinational, 39 (19%) were multinational, and 1 (<1%)
was continentwide. Among the 45 (16%) sub-Saharan African
studies, 20 (45%) were national, 6 (13%) were binational, 9
(20%) were trinational, and 10 (22%) were multinational.
Among the 14 (5%) East African studies, 2 (14%) were
national, 3 (22%) were binational, 2 (14%) were trinational,
and 7 (50%) were multinational. Of the 16 studies (6%)
conducted in West Africa, 10 (62%) were national, 3 (19%)
were binational, and 3 (19%) were multinational. Among the
7 RCTs from Southern Africa, 1 (14%) was national, 5 (72%)
were binational, and 1 (14%) was trinational.

**Conclusions** The scope of RCTs from Africa is rarely
continentwide, and 1 in 4 countries is not included.

**Conflict of Interest Disclosures** None reported.

![](https://assets.underline.io/uploads/markdown_image/1/image/f75455583977aa8739a26ddcba3353c7.png)

Geographical Scope of Randomized Clinical Trials From Africa

**Objective** To quantify the administrative demands and
timeliness of sharing individual participant data and identify
the main choke points for researchers and contracts
departments.

**Design** This mixed-methods study conducted in 2022
included 2 case studies of individual participant data meta-
analyses, Individual Participant Data on Cord Management at
Preterm Birth (iCOMP) and Transforming Obesity Prevention
for Children (TOPCHILD), for which data were requested
from international trials from 2019 until May 2022. Each
contact with trial investigators was logged in custom-built
software. From these logs, contact time points, study
characteristics (eg, year, sample size, country), email texts,
and data-sharing status were extracted. Researchers
responsible for negotiating agreements were interviewed to
identify choke points. The median, IQR, and range of time
from the first data request until data receipt were calculated,
and the number of emails required was tallied. Emails,
data-sharing agreements, reasons data could not be shared,
and interview responses were analyzed qualitatively using a
thematic analysis to identify choke points and facilitators.

**Results** For 71 of 72 included trials (50 iCOMP trials [69%],
22 TOPCHILD trials [31%]), data were directly requested and
supplied from investigators. Only 1 data set was available as a
publication supplement; none used data repositories. The
median (IQR) time from requesting to receiving data was 88
days (130 days). The longest completed request took 831 days
(ie, >2 years). A mean of 23 emails (SD, 25; range, 4-49
emails) were sent until data were received. While some data
were shared in the requested format (26 trials [36%]), other
data sets had to be recoded (46 trials [64%]), and substantial
contact with the investigators was required for this. Workload and duration to set up data sharing agreements were reduced
if institutions had executed agreements together previously.
For 3 studies, data were not shared despite investigators
being eager to do so because institutional approval could not
be obtained. For iCOMP, 7 requests remained in progress
after 854 days; for TOPCHILD, 9 requests were in progress
after 272 days. Most data sharing agreements were similar in
content but varied in detail and length (3-36 pages). The
largest choke points included differences in regulations across
jurisdictions and varying stakeholder expectations of rights
and responsibilities (eg, institutional expectations about data
storage, authorship rights). However, most emails dealt with
minor change requests and clarifications.

**Conclusions** This study’s results demonstrate major delays
in the process of data sharing from trials with requests
remaining unresolved after 2 years. The similarities identified
across agreements indicate opportunities for standardization.
Data sharing delays consume financial resources and impair
the timeliness of research, which is particularly problematic
for research endeavors linked to emergency situations (such
as the COVID-19 pandemic). Many researchers are willing, or
required by journals and funders, to share their data. To
overcome costly choke points and delays, streamlined
standards and infrastructure are needed.

**Funding/Support** Anna Lene Seidler receives research funding
support from a National Health and Medical Research Council
(NHMRC) investigator grant (GNT2009432). Kylie E. Hunter
receives research funding support via 2 scholarships administered
by the University of Sydney (Postgraduate Research Supplementary
Scholarship in Methods Development [SC3504] and Research
Training Program Stipend [SC3227]). TOPCHILD is supported by
an NHMRC ideas grant (GNT1186363); iCOMP is supported by an
NHMRC project grant (GNT1163585).

**Role of the Funder/Sponsor** The funders had no role in the
design and conduct of the study; collection, management, analysis,
and interpretation of the data; preparation, review, or approval of
the abstract; and decision to submit the abstract for presentation.

**Acknowledgment** We acknowledge all members of the iCOMP
collaboration (https://www.icompstudy.org/) and TOPCHILD
collaboration (https://www.opchildcollaboration
.org/) for their assistance with preparing and executing data
sharing agreements and sharing their data despite the barriers they
encountered.

![](https://assets.underline.io/uploads/markdown_image/1/image/46993b5bed231f8f05cc012aa43e1e8e.png)

Assessment of Time and Resources Required to Share Data for 2 Individual Participant Data Meta-analyses

**Objective** To evaluate whether peer review of diagnostic test
accuracy (DTA) studies published by imaging journals is
associated with changes in completeness of reporting,
transparency of risk of bias, and spin, given that there is
limited evidence to support the concept that peer review
improves the completeness of research reporting.1,2 
 **Design** This retrospective cross-sectional study evaluated
articles published in the Journal of Magnetic Resonance
Imaging (JMRI; 2019 impact factor [IF], 4.0), the Canadian
Association of Radiologists Journal (CARJ; IF, 1.7), and
European Radiology (EuRad; IF, 4.1) before March 31,
2020.3 Initial submitted and final versions of manuscripts
were screened consecutively in reverse chronological order to
include a minimum of 23 articles (based on power
calculation) per journal. At least 30 eligible articles from each
journal were collected when available to account for potential
exclusions. Primary studies evaluating the diagnostic
accuracy of an imaging test in humans were included. Studies
exclusively reporting on prognostic or predictive tests were
excluded. Studies were evaluated independently by 2
reviewers blinded to version for completeness of reporting
using the Standards for Reporting Diagnostic Accuracy
Studies (STARD) 2015 and STARD for Abstracts guidelines,
transparency of reporting for risk of bias assessment based on
the Quality Assessment of Diagnostic Accuracy Studies-2
(QUADAS-2), and actual and potential spin using modified
published criteria. Two-tailed paired t-tests and paired
Wilcoxon signed-rank tests were used for comparisons;
P < .05 was considered statistically significant. 
 **Results** Of 692 diagnostic accuracy studies screened, 84
articles published in 2014 to 2020 from 3 journals were
included: JMRI, 30 articles; CARJ, 23; and EuRad, 31.
Reporting by STARD 2015 increased between initial
submissions and final accepted versions (mean reported
items 16.67 vs 17.47; change, 0.80 [95% CI, 0.25 to 1.17];
P = .002). From STARD, sources of funding and other
support (item 30.1) and role of funders (item 30.2) had the
largest change of 0.32 (P < .001). No difference was found for
the reporting of STARD for Abstracts (5.28 vs 5.25; change,
−0.03; 95% CI, −0.15 to 0.11; P = .74); QUADAS-2 (6.08 vs
6.11; 0.03; 95% CI, −1.00 to 0.50; P = .92); actual spin (2.36
vs 2.40; change, 0.04; 95% CI, 0.00 to 1.00; P = .39); or
potential spin practices (2.93 vs 2.81; change, −0.12; 95% CI,
−1.00 to 0.00; P = .23) (**Figure 20**). 
 ![](https://assets.underline.io/uploads/markdown_image/1/image/54b0fafbe137d88e35b76a7ddac25b9a.png) 
 **Conclusions** This retrospective cross-sectional study found
that peer review was associated with a marginal improvement
in completeness of full text; however, it was not associated
with abstract reporting in published imaging DTA studies nor
with improvement in transparency for risk of bias assessment
or reduction in spin. Considering that this study included
articles from only 3 radiology journals, the findings may not
be generalizable to other journals, other fields of DTA
research, or non-DTA study designs. Interventions such as
reviewer training and use of checklists should be evaluated. 
 **References**
1.	Jefferson T, Rudin M, Brodney Folse S, Davidoff F. Editorial peer review for improving the quality of reports of biomedical studies. Cochrane Library. 2020. doi:10.1002/14651858.MR000016.pub3 
 2.	Bruce R, Chauvin A, Trinquart L, Ravaud P, Boutron I. Impact of interventions to improve the quality of peer review of biomedical journals: a systematic review and metaanalysis. BMC Medicine. 2016;14(1):1-16. doi:10.1186/ s12916-016-0631-5 
 3.	Clarivate Analytics. Journal Citation Reports. 2019 Journal Impact Factor. Accessed July 11, 2020. https://clarivate.com/blog/announcing-the-2019-journal-citation-reports/ 
 **Conflict of Interest Disclosures** Mark Schweitzer, Yves Menu,
Michael Patlas, and Kelly D. Cobey have active affiliations with the
3 journals used as data sources but had no role in data extraction,
analysis, or interpretation, but reviewed and approved the work.
Michael Patlas reported an editorial honorarium from Springer
outside of the submitted work. No other disclosures were reported. 
 **Funding/Support Funding** support was received from the
Philips−Radiological Society of North America research seed
grant (RSNA Research & Education Foundation), Mitacs Research
Training Award, and the Department of Radiology MD Summer
Student Fund at the University of Ottawa. Study performance and
manuscript content were the sole task and responsibility of the
investigators and do not necessarily represent the official views of
the funders. 
 **Role of the Funder/Sponsor** The funders had no role in data
collection, analysis, interpretation, or manuscript composition. 
 **Acknowledgments** Sakib Kazi and Robert A. Frank contributed
equally to this work. 
 ![](https://assets.underline.io/uploads/markdown_image/1/image/0407c7b1d6b20790b2200ea4ae9cd9e1.png)

Association of Peer Review with Completeness of Reporting, Transparency for Risk of Bias, and Spin in Diagnostic Test Accuracy Studies Published in Imaging Journals

**Objective** Because randomized clinical trials (RCTs) inform
clinical guidelines and can directly influence patient care, it is
important to ensure that the data behind their conclusions
are trustworthy. We aimed to develop a checklist to screen
RCTs for possible data fabrication at submission or prior to
inclusion in meta-analyses.

**Design** The development of this screening tool was adapted
from the 4-stage approach proposed by Moher et al1
 for
reporting guidelines, including defining the scope, reviewing
the evidence base, suggesting a list of items from piloting, and
holding a consensus meeting as part of a Delphi method. The initial checklist was set up by a smaller core group based on
the authors’ experience assessing problematic RCTs for
several years. The checklist was then piloted in a Delphi panel
of 20 stakeholders, including clinicians, reviewers, journal
editors, and evidence synthesis specialists. Using a set of 15
articles, 8 of which were known to have fabricated data, each
member was asked to score 3 articles with the checklist.
Results were then discussed in 2 Delphi sessions.

**Results** The screening checklist had 7 domains and is
detailed in **Table 48**. The group proposed that a positive
screen be defined by 2 or more items present.

![](https://assets.underline.io/uploads/markdown_image/1/image/8537c01e907b0219592b5662529fc040.png)

**Conclusions** This is the first checklist developed in a formal
process to detect possible data fabrication in RCTs. If a study
is assessed and found to be suspicious, reviewers can consider
a more thorough investigation into the data integrity issues
identified, including assessment of original data. This
checklist may help editors, publishers, and researchers to
screen for data fabrication in RCTs in an objective manner.

**Reference**
1. Moher D, Schulz KF, Simera I, Altman DG. Guidance for
developers of health research reporting guidelines. PLOS
Medicine. 2010;7(2):e1000217. doi.org/10.1371/journal.
pmed.1000217

**Conflict of Interest Disclosures** Ben W. Mol is supported by the
National Health and Medical Research Council (grant GNT1176437),
reports consultancy for ObsEva and Merck, and receives travel
support from Merck. No other disclosures were reported.

![](https://assets.underline.io/uploads/markdown_image/1/image/91e7149ec0ef1fb3743382b33ffb142e.png)

A Screening Checklist to Assess Data Integrity and Fabrication in Randomized Clinical Trials

**Objective** The National Institutes of Health (NIH) is the
largest source of government funding for biomedical research
in the world, although only a small proportion of its funding
is dedicated to pediatric research. Timely dissemination of
results for NIH-funded pediatric clinical trials is imperative
to uphold the integrity of scientific evidence, maintain ethical
obligations to trial participants, and ensure evidence-based
clinical care. Since 2017, the NIH has required NIH-funded
clinical trials to be registered and summary results submitted
to ClinicalTrials.gov, generally not later than 1 year after
primary trial completion.1 In this study, the dissemination of
NIH-funded pediatric clinical trial findings was investigated
through results submitted to ClinicalTrials.gov and in
publications in peer-reviewed medical journals. 
 **Design** A cross-sectional analysis of NIH grants funding
pediatric clinical trials with funding completed from January
1, 2017, to December 31, 2019, was conducted. Grants funding
pediatric trials in NIH RePORTER and the status of
registration and results submission in ClinicalTrials.gov were
determined. Publications were identified in PubMed as of
February 28, 2022. Time to results reporting in ClinicalTrials.
gov at 12 months and 24 months since primary trial
completion, and factors associated with nonpublication from
an a priori set of variables using multivariable logistic
regression were determined. 
 **Results** Among 3408 pediatric grants completed during the
study period, 421 (12.4%) supported an interventional study
(**Table 15**). Mental and behavioral health conditions (70
trials [16.6%]), obesity (56 [13.3%]), and substance use (40
[9.5%]) were the most commonly studied conditions. Greater
than three-quarters of the trials studied behavioral
interventions (321 [76.2%]). There were 360 trials (85.5%)
registered in ClincialTrials.gov, of which 229 (63.6%) were
registered prospectively (ie, within 21 days of study start).
Results were submitted for 16.6% of trials (95% CI, 14.4%-
18.8%) by 12 months and 22.8% (95% CI, 20.2%-25.5%) by
24 months. Publications were available for 11.8% of trials
(95% CI, 10.2%-13.5%) at 12 months and 27.7% (95% CI,
25.3%-30.1%) at 24 months. Overall, 56,833 participants
were enrolled in trials that remained unpublished after a
median follow-up of 33 months since trial completion. Trials
were more likely to remain unpublished if the trial end point
was a surrogate measure (adjusted odds ratio, 2.09; 95% CI,
1.26-3.45) compared with a clinical event.
Conclusions Despite policies promoting dissemination of
NIH-funded clinical trial results, less than a third of pediatric
trials reported results in ClinicalTrials.gov or published
findings in peer-reviewed journals 2 years after completion.
To maximize the impact of pediatric clinical trials, additional
efforts are needed to improve reporting practices and advance
translation of research findings into evidence-based clinical
care. Such efforts may include the withholding of additional
funding unless trials have been prospectively registered and
results publicly reported in a timely manner. 
 ![](https://assets.underline.io/uploads/markdown_image/1/image/8741472e7e3b10bfb2b1579d368a216d.png) 
 **Reference**
1. NIH policy on the dissemination of NIH-funded clinical
trial information. National Institutes of Health. Updated July
24, 2017. Accessed March 5, 2022. https://grants.nih.gov/policy/clinical-trials/reporting/understanding/nih-policy.htm 
 **Conflict of Interest Disclosures** No authors have potential
conflicts of interest, including specific financial interests and
relationships and affiliations relevant to the subject of this abstract. 
 ![](https://assets.underline.io/uploads/markdown_image/1/image/0118e26879330c6e58d8b8c585f4bc88.png)

Dissemination of the Results of Pediatric Clinical Trials Funded by the US National Institutes of Health

**Objective** High scientific quality produces knowledge and is continuously assessed by the scientific community in multiple ways (eg, reviews, seminars, committees that control funding, publication of research results, and selection of individuals for awards, employment, and other positions). The quality of these assessments affects the quality of the research produced.1,2 So how valid and reliable are they, and what weight do academics attach to different features of the research in their assessment of scientific quality? 
 **Design** A rating instrument was devised with 36 concrete and 2 overall quality indicators (**Table 12**), partly based on the evaluation criteria in scientific journals’ instructions for reviewers. Fifteen academics with a PhD in the social sciences were recruited through snowball sampling. They rated 60 randomly selected journal articles from the Swedish Gender Studies List of more than 12,000 publications about sex and gender across many disciplines (eg, gender studies [GS], sociology, history, medicine, and psychology) published between 2000 and 2010. Factor analysis was applied to identify the main dimensions that reviewers consider, and these were compared across research with different levels of GS perspective.3 A high level of GS perspective was assigned to articles authored by those who self-identified as gender scholars or explicitly endorsed a gender perspective, a medium level to articles that reflected these values and beliefs, and a low level to the remaining articles. Statistical tests included Cronbach α for interrater reliability, R2 for factor analysis, and Cohen d and P values for differences between GS groups. 
 ![](https://assets.underline.io/uploads/markdown_image/1/image/face4de9825e4d15726c834490017978.png) 
 **Results** Interrater reliability was high (Cronbach α = 0.75-0.87). Factor analysis suggested 3 dimensions: logic and clarity (eg, “there is a clear connection between theory and empirics, such that the data logically correspond to what is being claimed, hypothesized, or tested”; R2 = 0.44); causality (eg, “a causal relation is mentioned or can be inferred, and the design and methods in general enable this to be evaluated”; R2 = 0.08); and scope (eg, more data, units of analysis, generalizability, and better sampling; R2 = 0.10). The analysis could discriminate between 3 samples of 20 randomly selected articles from each of the populations with (1) a high level of GS perspective, (2) a medium level, and (3) no GS perspective on these 3 dimensions (logic and clarity, d = 0.62, P < .001; causality, d = 0.35, P < .005; and scope, d = 
0.44, P < .005). 
 **Conclusions** The scientific quality instrument seems promising in terms of high reliability and convergent validity and can be used in any type of design (eg, experimental, cross-sectional, correlational, or descriptive) to assess overall and specific dimensions of scientific quality. Additional testing of the instrument is needed to compare different bodies of research, funding applications, project plans, and 
papers before and after peer review; assess group differences across topics, institutions, countries, and time; and compare mainstream vs controversial fields of research to further assess the role of research quality. 
 **References**
1.	Finkel EJ, Eastwick PW, Reis HT. Replicability and other features of a high-quality science: toward a balanced and empirical approach. J Pers Soc Psychol. 2017;113(2):244-253. 
doi:10.1037/pspi0000075 
 2.	Tuffaha HW, El Saifi N, Chambers SK, Scuffham PA. Directing research funds to the right research projects: a review of criteria used by research organisations in Australia in prioritising health research projects for funding. BMJ Open. 2018;8(12):e026207. doi:10.1136/ bmjopen-2018-026207 
 3.	Söderlund T, Madison G. Objectivity and realms of explanation in academic journal articles concerning sex/ gender: a comparison of gender studies and the other social sciences. Scientometrics. 2017;112(2):1093-1109. doi:10.1007/s11192-017-2407-x 
 **Conflict of Interest Disclosures** None reported. 
 ![](https://assets.underline.io/uploads/markdown_image/1/image/5c40d8638b8069d3c57f909b01c9a9f0.png)

Factor Analysis of Academic Reviewers Ratings of Journal Articles on a 38-Item Scientific Quality Instrument | VIDEO

This study contributes to analyze the mechanisms through which high performance work systems (HPWS) might affect job satisfaction in the hospitality sector. We build on the mutual gains perspective of the human resources management (HRM) literature to hypothesize that HPWS are conducive for higher levels of job satisfaction in hotel workers. Besides, and based on the job demands-resources model, we argue that HPWS are beneficial in hospitality firms, as they provide workers with resources to adapt to their job demands, alleviating the stress derived from the intense workloads, which are typical in hotels. Thus, we believe that HPWS contribute to reduce workers' burnout, which may in part improve their job satisfaction. Analysis from surveyed hotel employees (N = 202) revealed the positive effects of HPWS on job satisfaction, as well as their role in reducing burnout. Through partial least square structural equation modelling we corroborate the direct positive effect on job satisfaction and the indirect effect through the reduction of burnout. Under the abilities-motivation-opportunities (AMO) framework we identified the effects of the different HRM "bundles of practices" on job satisfaction and burnout. Concretely, the "abilities" bundle are conducive for higher job satisfaction, whereas both the "motivations" and the "opportunities" bundles of practices reduce burnout. This study offers the first evidence of burnout as a mediating mechanism in the so-called black box of HPWS, with application to the tourism and hospitality context. It also contributes to the field with important theoretical contributions, as well as managerial recommendations.

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End-to-end Entity Linking with Hierarchical Reinforcement Learning

Development of the Standards for Reporting Subtyping Studies (StaRSS) Reporting Guideline

Geographical Scope of Randomized Clinical Trials From Africa

Assessment of Time and Resources Required to Share Data for 2 Individual Participant Data Meta-analyses

Association of Peer Review with Completeness of Reporting, Transparency for Risk of Bias, and Spin in Diagnostic Test Accuracy Studies Published in Imaging Journals

A Screening Checklist to Assess Data Integrity and Fabrication in Randomized Clinical Trials

Dissemination of the Results of Pediatric Clinical Trials Funded by the US National Institutes of Health

Factor Analysis of Academic Reviewers Ratings of Journal Articles on a 38-Item Scientific Quality Instrument | VIDEO

High performance work systems for employees’ job satisfaction: The mediational role of employees’ burnout

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